code
This commit is contained in:
jiangchaoqing
parent
70d36c620c
commit
fa0209c3b3
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#!/bin/bash
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# 定义日志目录
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LOG_DIR="/home/th/jcq/AI_AutoPlat/logs"
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mkdir -p $LOG_DIR
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# 定义要运行的程序及其工作目录
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declare -A PROGRAMS=(
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["web_dsj"]="/home/th/jcq/AI_AutoPlat/AI_web_dsj/AI_auto_train.py"
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["yolov5"]="/home/th/jcq/AI_AutoPlat/yolov5-th/ai_auto_train_yolov5.py"
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)
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declare -A WORK_DIRS=(
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["web_dsj"]="/home/th/jcq/AI_AutoPlat/AI_web_dsj"
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["yolov5"]="/home/th/jcq/AI_AutoPlat/yolov5-th"
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)
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# 启动所有程序
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for name in "${!PROGRAMS[@]}"; do
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program="${PROGRAMS[$name]}"
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work_dir="${WORK_DIRS[$name]}"
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log_file="$LOG_DIR/${name}_$(date +%Y%m%d_%H%M%S).log"
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echo "Starting $name (working dir: $work_dir)..."
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(
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cd "$work_dir" || exit 1
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nohup /home/th/anaconda3/envs/yolov5/bin/python "$(basename "$program")" >> "$log_file" 2>&1 &
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echo "PID: $! | Log: $log_file"
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)
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done
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echo "All services started in background"
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echo "To check running processes: ps aux | grep 'python'"
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echo "To view logs: tail -f $LOG_DIR/*.log"
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@ -0,0 +1,7 @@
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#!/bin/bash
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# 停止所有相关Python进程
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pkill -f "AI_auto_train.py"
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pkill -f "ai_auto_train_yolov5.py"
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echo "All AI training services stopped"
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File diff suppressed because it is too large
Load Diff
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import torch
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import cv2
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import numpy as np
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import os
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import json
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from minio import Minio
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from minio.error import S3Error
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from PIL import Image
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import io
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from urllib.parse import urlparse
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from PIL import Image, ImageDraw, ImageFont
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import numpy as np
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# MinIO configuration
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MINIO_ENDPOINT = "minio-jndsj.t-aaron.com:2443"
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MINIO_ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2"
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MINIO_SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI"
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MINIO_SECURE = False # Set to True if using HTTPS
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MODEL_DICT_CONFIG = {
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"001": "river",
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"002": "forest2",
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"003": "highWay2",
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"006": "vehicle",
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"007": "pedestrian",
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"008": "smogfire",
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"009": "AnglerSwimmer",
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"010": "countryRoad",
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"011": "ship2",
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"013": "channelEmergency",
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"014": "forest2",
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"015": "river2",
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"016": "cityMangement2",
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"017": "drowning",
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"018": "noParking",
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"019": "illParking",
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"020": "cityRoad",
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"023": "pothole",
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"024": "channel2",
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"025": "riverT",
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"026": "forestCrowd",
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"027": "highWay2T",
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"028": "smartSite",
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"029": "rubbish",
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"030": "firework"
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}
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def get_minio_client():
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"""Create and return a MinIO client instance"""
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return Minio(
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MINIO_ENDPOINT,
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access_key=MINIO_ACCESS_KEY,
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secret_key=MINIO_SECRET_KEY,
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secure=MINIO_SECURE
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)
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def download_from_minio(bucket_name, object_path, save_path):
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"""Download a file from MinIO storage"""
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try:
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client = get_minio_client()
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client.fget_object(bucket_name, object_path, save_path)
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return True
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except S3Error as err:
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print(f"MinIO Error: {err}")
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return False
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except Exception as e:
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print(f"Error downloading from MinIO: {e}")
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return False
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def download_image_from_minio(bucket_name, object_path):
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"""Download an image from MinIO and return as OpenCV format"""
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try:
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client = get_minio_client()
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response = client.get_object(bucket_name, object_path)
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image_data = response.read()
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image = Image.open(io.BytesIO(image_data))
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return cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
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except S3Error as err:
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print(f"MinIO Error: {err}")
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return None
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except Exception as e:
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print(f"Error downloading image from MinIO: {e}")
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return None
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finally:
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if 'response' in locals():
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response.close()
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response.release_conn()
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def parse_minio_url(url):
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"""Parse MinIO URL to extract bucket and object path"""
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parsed = urlparse(url)
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if not parsed.netloc.endswith("minio-jndsj.t-aaron.com:2443"):
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raise ValueError("URL is not from MinIO storage")
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bucket = parsed.path.split('/')[1]
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# bucket = parsed.netloc.split('.')[0]
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# object_path = parsed.path.lstrip('/')
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parts = parsed.path.strip("/").split("/")
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object_path = "/".join(parts[1:]) + "/"
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return bucket, object_path
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def parse_minio_image_url(url):
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"""
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解析MinIO图片URL，提取bucket名称和object路径
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Args:
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url (str): MinIO图片URL (e.g. "http://172.18.0.129:8084/th-dsp/123/image.jpg")
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Returns:
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tuple: (bucket_name, object_path)
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"""
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parsed = urlparse(url)
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if not parsed.netloc.endswith("minio-jndsj.t-aaron.com:2443"):
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raise ValueError("URL is not from MinIO storage")
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# 分割路径部分
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path_parts = [p for p in parsed.path.split('/') if p] # 移除空字符串
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if len(path_parts) < 2:
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raise ValueError("Invalid MinIO URL format")
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bucket = path_parts[0] # 第一个非空部分是bucket名称
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object_path = '/'.join(path_parts[1:]) # 剩余部分是object路径
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return bucket, object_path
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def save_image_to_url1(image, output_path):
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"""Save image to MinIO storage"""
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try:
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# First save locally
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local_path = f"temp_{os.path.basename(output_path)}"
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cv2.imwrite(local_path, image)
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# Upload to MinIO
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bucket_name, object_path = parse_minio_url(output_path)
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# minio_path = os.path.dirname(object_path).split('/')[0]
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minio_path = object_path.rstrip('/')
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print("###line159",minio_path)
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# 配置信息
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ENDPOINT = "minio-jndsj.t-aaron.com:2443"
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ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2"
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SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI"
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# 创建 MinIO 客户端
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client = Minio(
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ENDPOINT,
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access_key=ACCESS_KEY,
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secret_key=SECRET_KEY,
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secure=True # 使用 HTTPS
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)
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client.fput_object(bucket_name, minio_path, local_path)
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# # Clean up local file
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# os.remove(local_path)
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return output_path
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except Exception as e:
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print(f"Error saving image to MinIO: {e}")
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return None
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def download_minio_files(endpoint, access_key, secret_key, bucket_name, prefix='', local_dir='downloads'):
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try:
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# 创建MinIO客户端
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client = Minio(
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endpoint,
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access_key=access_key,
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secret_key=secret_key,
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secure=False
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)
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# 确保本地目录存在
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local_dir = os.path.join(local_dir,prefix)
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# os.makedirs(local_dir, exist_ok=True)
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# /home/thsw/WJ/jcq/yolov5_servers/AI_web/api_task/downloaded_weights/weights/026/v1.0/channl2_0416.pt
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# 列出对象
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objects = client.list_objects(bucket_name, prefix=prefix, recursive=True)
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print(f"Downloading files from bucket '{bucket_name}' with prefix '{prefix}':")
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print("-" * 50)
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# 遍历并下载所有对象
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for obj in objects:
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# 构建本地文件路径
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local_path = os.path.join(local_dir, os.path.relpath(obj.object_name, prefix))
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# 确保目录存在
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os.makedirs(os.path.dirname(local_path), exist_ok=True)
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# 下载文件
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client.fget_object(bucket_name, obj.object_name, local_path)
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print(f"Downloaded: {obj.object_name} -> {local_path}")
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print(f"Size: {obj.size} bytes")
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print(f"Last Modified: {obj.last_modified}")
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print("-" * 50)
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return local_path
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except S3Error as err:
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print(f"MinIO Error occurred: {err}")
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except Exception as e:
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print(f"Error occurred: {e}")
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def download_minio_files1(endpoint, access_key, secret_key, bucket_name,prefix='', local_dir='downloaded_weights'):
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# 创建 MinIO 客户端
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client = Minio(
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endpoint,
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access_key=access_key,
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secret_key=secret_key,
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secure=True # 使用 HTTPS
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)
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try:
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# 确保本地目录存在
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os.makedirs(local_dir, exist_ok=True)
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# 列出文件夹中的所有对象
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objects = client.list_objects(bucket_name, prefix=prefix, recursive=True)
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for obj in objects:
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# 构建本地文件路径
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local_path = os.path.join(local_dir, os.path.relpath(obj.object_name, prefix))
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# 确保子目录存在
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os.makedirs(os.path.dirname(local_path), exist_ok=True)
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# 下载文件
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print(f"正在下载: {obj.object_name} -> {local_path}")
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client.fget_object(bucket_name, obj.object_name, local_path)
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print("所有文件下载完成!")
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print("所有文件下载完成!")
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return local_path
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except S3Error as e:
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print(f"MinIO 错误: {e}")
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except Exception as e:
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print(f"发生错误: {e}")
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def download_minio_image(endpoint, access_key, secret_key, bucket_name, prefix='', local_dir='downloaded_images'):
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try:
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# 创建MinIO客户端
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client = Minio(
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endpoint,
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access_key=access_key,
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secret_key=secret_key,
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secure=False
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)
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print(f"Downloading image from bucket '{bucket_name}': {bucket_name}")
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print("-" * 50)
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# # 构建本地保存路径 (保持原目录结构)
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# local_path = os.path.join(local_dir, prefix)
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# # os.makedirs(os.path.dirname(local_path), exist_ok=True)
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local_file_path = os.path.join(local_dir, bucket_name, prefix)
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# 下载文件
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client.fget_object(bucket_name, prefix, local_file_path)
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print(f"Successfully downloaded to: {local_file_path}")
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print("-" * 50)
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return local_file_path
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except S3Error as err:
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print(f"MinIO Error occurred: {err}")
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except Exception as e:
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print(f"Error occurred: {e}")
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def download_minio_image1(endpoint, access_key, secret_key, bucket_name, prefix='', local_dir='downloaded_images'):
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# 配置信息
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ENDPOINT = "minio-jndsj.t-aaron.com:2443"
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ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2"
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SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI"
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try:
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# 创建 MinIO 客户端
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client = Minio(
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ENDPOINT,
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access_key=ACCESS_KEY,
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secret_key=SECRET_KEY,
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secure=True # 使用 HTTPS
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)
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# 下载文件
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print(f"正在下载: {bucket_name}/{prefix} -> {local_dir}")
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client.fget_object(bucket_name, prefix, local_dir)
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print("文件下载完成!")
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return local_dir
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except S3Error as err:
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print(f"MinIO Error occurred: {err}")
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except Exception as e:
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print(f"Error occurred: {e}")
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def get_model_name(code):
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code_str = f"{int(code):03d}" if str(code).isdigit() else code
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return MODEL_DICT_CONFIG.get(code_str, f"Unknown code: {code}")
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def infer_yolov5(input_json):
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# 中文名称映射
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conf_labels = "/home/th/jcq/AI_AutoPlat/AI_web_dsj/config/conf_labels"
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try:
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# Parse input JSON
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request_id = input_json.get("request_id", "")
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model_code = input_json.get("ModelCode", "")
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image_url = input_json.get("ValImageDir", "")
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model_version_url = input_json.get("ModelVersion", "")
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model_name = get_model_name(model_code)
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print("####line301",model_name)
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# Create temp directory
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LOCAL_DIR = "downloaded_weights" # 本地存储目录
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os.makedirs("LOCAL_DIR", exist_ok=True)
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# Download model from MinIO
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model_bucket, model_object_path = parse_minio_url(model_version_url)
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print("###line290",model_bucket,model_object_path)
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# 配置信息
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ENDPOINT = "minio-jndsj.t-aaron.com:2443"
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ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2"
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SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI"
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model_path = download_minio_files1(
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endpoint=ENDPOINT,
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access_key=ACCESS_KEY,
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secret_key=SECRET_KEY,
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bucket_name=model_bucket,
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prefix=model_object_path,
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local_dir=LOCAL_DIR
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)
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print("line265",model_path)
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# Download image from MinIO
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image_bucket, image_object_path = parse_minio_image_url(image_url)
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print("###line416",image_bucket,image_object_path)
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filename = os.path.basename(image_object_path) # 提取文件名
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print(filename) # 输出: 27ffd870-a417-4b65-8ec6-76dd5b2693fe.png
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# Create temp directory
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LOCAL_IMAGE_DIR = "downloaded_images" # 本地存储目录
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os.makedirs("LOCAL_IMAGE_DIR", exist_ok=True)
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# dst_img = download_minio_image1(
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# endpoint=MINIO_ENDPOINT,
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# access_key=MINIO_ACCESS_KEY,
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# secret_key=MINIO_SECRET_KEY,
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# bucket_name=image_bucket,
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# prefix=image_object_path,
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# local_dir=LOCAL_IMAGE_DIR
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# )
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dst_img = download_minio_image1(
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endpoint=MINIO_ENDPOINT,
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access_key=MINIO_ACCESS_KEY,
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secret_key=MINIO_SECRET_KEY,
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bucket_name=image_bucket,
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prefix=image_object_path,
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local_dir=filename
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)
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|
||||
# dst_img = '/home/thsw/WJ/jcq/yolov5_servers/AI_web/api_task/downloaded_images/th-dsp/123/bcf66d91-ee2c-4aca-9b8d-5d5a7a655146.jpg'
|
||||
print("line302",dst_img)
|
||||
|
||||
device = torch.device('cpu')
|
||||
|
||||
# 加载模型并指定 GPU
|
||||
model = torch.hub.load('/home/th/jcq/AI_AutoPlat/AI_web_dsj/ultralytics/yolov5',
|
||||
'custom',
|
||||
path=model_path,
|
||||
source='local',
|
||||
force_reload=False,
|
||||
skip_validation=True,
|
||||
device=device) # 关键：指定设备
|
||||
|
||||
|
||||
# 设置置信度阈值和IOU阈值
|
||||
model.conf = 0.5 # 置信度阈值，默认0.25，高于此值的检测结果才会保留
|
||||
model.iou = 0.45 # NMS的IOU阈值，默认0.45
|
||||
|
||||
# Run inference
|
||||
results = model(dst_img)
|
||||
|
||||
# 获取检测框信息
|
||||
boxes = results.xyxy[0].cpu().numpy()
|
||||
|
||||
|
||||
|
||||
names = model.names
|
||||
|
||||
# 存储所有检测目标的信息
|
||||
check_task_details = []
|
||||
|
||||
print("######line400",len(boxes))
|
||||
|
||||
if len(boxes) == 0:
|
||||
|
||||
annotated_img = cv2.imread(dst_img)
|
||||
# Save annotated image to MinIO
|
||||
output_image_name = f"{request_id}_AI.jpg"
|
||||
output_image_path = os.path.join("temp", output_image_name)
|
||||
|
||||
cv2.imwrite(output_image_path, annotated_img)
|
||||
|
||||
# Upload to MinIO
|
||||
ai_file_image_url = f"http://172.18.0.129:8084/th-dsp/results/{output_image_name}"
|
||||
|
||||
print("###line498",ai_file_image_url)
|
||||
|
||||
# ai_file_image_url = f"http://172.18.0.129:8084/th-dsp/results/"
|
||||
if not save_image_to_url1(annotated_img, ai_file_image_url):
|
||||
raise Exception("Failed to upload result image to MinIO")
|
||||
|
||||
|
||||
callback_data = {
|
||||
"id": request_id,
|
||||
"status": 2,
|
||||
"aiFileImage": ai_file_image_url,
|
||||
"checkTaskDetails": []
|
||||
}
|
||||
|
||||
return callback_data
|
||||
|
||||
# 用于记录每个类别出现的次数（用于生成唯一名称）
|
||||
class_counters = {}
|
||||
|
||||
# 读取图像用于绘制
|
||||
annotated_img = cv2.imread(dst_img)
|
||||
height, width, _ = annotated_img.shape
|
||||
|
||||
# model_labels_path = os.path.join(conf_labels,model_name)
|
||||
model_labels_path = os.path.join(conf_labels,model_name) + "/labelnames.json"
|
||||
|
||||
|
||||
print("####404",model_labels_path)
|
||||
|
||||
|
||||
|
||||
# # 假设我们已经加载了labelsnames.json
|
||||
# label_mapping = {
|
||||
# "labelnames_实际": ["国旗", "浮标", "船名", "船只", "未挂国旗船只"],
|
||||
# "labelnames": ["国旗", "浮标", "船名", "船只", "未挂国旗船只"],
|
||||
# "labelIndexs": ["SL040", "SL041", "SL042", "SL043", "SL044"]
|
||||
# }
|
||||
|
||||
|
||||
|
||||
|
||||
with open(model_labels_path, 'r', encoding='utf-8') as f:
|
||||
label_mapping = json.load(f)
|
||||
print("成功加载标签映射文件:", label_mapping)
|
||||
|
||||
|
||||
font_path = "/home/th/jcq/AI_AutoPlat/AI_web_dsj/config/Simhei.ttf"
|
||||
|
||||
for i, box in enumerate(boxes):
|
||||
x1, y1, x2, y2, conf, cls_id = box
|
||||
|
||||
# 获取中文类别名称
|
||||
try:
|
||||
cls_name = label_mapping["labelnames"][int(cls_id)]
|
||||
except IndexError:
|
||||
cls_name = f"未知类别_{int(cls_id)}"
|
||||
|
||||
# 更新类别计数器
|
||||
if cls_name not in class_counters:
|
||||
class_counters[cls_name] = 1
|
||||
else:
|
||||
class_counters[cls_name] += 1
|
||||
|
||||
unique_name = f"{cls_name}_{class_counters[cls_name]}"
|
||||
|
||||
# 绘制边界框（仍然使用OpenCV）
|
||||
cv2.rectangle(annotated_img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
|
||||
|
||||
# 将OpenCV图像转换为Pillow图像
|
||||
img_pil = Image.fromarray(cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB))
|
||||
draw = ImageDraw.Draw(img_pil)
|
||||
|
||||
try:
|
||||
# 尝试加载字体
|
||||
font = ImageFont.truetype(font_path, 20)
|
||||
except:
|
||||
# 如果找不到字体，使用默认字体（可能不支持中文）
|
||||
font = ImageFont.load_default()
|
||||
|
||||
# 绘制中文文本
|
||||
label = f"{unique_name}: {conf:.2f}"
|
||||
draw.text((int(x1), int(y1) - 25), label, font=font, fill=(0, 255, 0, 0))
|
||||
|
||||
# 转换回OpenCV格式
|
||||
annotated_img = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)
|
||||
|
||||
# 为每个检测目标生成一条记录
|
||||
detection_info = {
|
||||
"dspModelCode": model_code,
|
||||
"dspModelName": unique_name,
|
||||
"possibility": f"{int(conf * 100)}%",
|
||||
"bbox": {
|
||||
"x1": float(x1),
|
||||
"y1": float(y1),
|
||||
"x2": float(x2),
|
||||
"y2": float(y2),
|
||||
},
|
||||
"detection_id": i,
|
||||
"labelIndex": label_mapping["labelIndexs"][int(cls_id)]
|
||||
}
|
||||
|
||||
check_task_details.append(detection_info)
|
||||
|
||||
|
||||
|
||||
|
||||
# Save annotated image to MinIO
|
||||
output_image_name = f"{request_id}_AI.jpg"
|
||||
output_image_path = os.path.join("temp", output_image_name)
|
||||
|
||||
|
||||
cv2.imwrite(output_image_path, annotated_img)
|
||||
|
||||
# Upload to MinIO
|
||||
ai_file_image_url = f"https://minio-jndsj.t-aaron.com:2443/th-dsp/results/{output_image_name}"
|
||||
|
||||
|
||||
if not save_image_to_url1(annotated_img, ai_file_image_url):
|
||||
raise Exception("Failed to upload result image to MinIO")
|
||||
|
||||
|
||||
|
||||
# Prepare output
|
||||
callback_data = {
|
||||
"id": request_id,
|
||||
"status": 2,
|
||||
"aiFileImage": ai_file_image_url,
|
||||
"checkTaskDetails": check_task_details
|
||||
}
|
||||
|
||||
# # Clean up temp files
|
||||
# os.remove(model_path)
|
||||
# os.remove(output_image_path)
|
||||
|
||||
return callback_data
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"id": request_id,
|
||||
"status": 3, # Error status
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
# Example usage
|
||||
if __name__ == "__main__":
|
||||
input_json = {
|
||||
"ValParmeter": "",
|
||||
"ModelCode": "024",
|
||||
"ValImageDir": "https://minio-jndsj.t-aaron.com:2443/th-dsp/123/27ffd870-a417-4b65-8ec6-76dd5b2693fe.png",
|
||||
"Errorcode": "",
|
||||
"ModelScenetype": "check",
|
||||
"ModelVersion": "https://minio-jndsj.t-aaron.com:2443/algorithm/weights/024/V1.0/",
|
||||
"request_id": "e3b3708f-a328-401c-a043-27439f8a38d7"
|
||||
}
|
||||
|
||||
output = infer_yolov5(input_json)
|
||||
print(json.dumps(output, indent=2))
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
|
||||
001: AnglerSwimmer
|
||||
002: channel2
|
||||
003: channelEmergency
|
||||
004: cityMangement
|
||||
005: cityMangement2
|
||||
006: cityMangement3
|
||||
007: cityRoad
|
||||
008: conf
|
||||
009: countryRoad
|
||||
010: crackMeasurement
|
||||
011: crowdCounting
|
||||
012: drowning
|
||||
013: forest
|
||||
014: forest2
|
||||
015: forestCrowd
|
||||
016: highWay2
|
||||
017: illParking
|
||||
018: noParking
|
||||
019: ocr2
|
||||
020: ocr_en
|
||||
021: pedestrian
|
||||
022: pothole
|
||||
023: river
|
||||
024: river2
|
||||
025: riverT
|
||||
026: road
|
||||
027: ship
|
||||
028: ship2
|
||||
029: smogfire
|
||||
030: trafficAccident
|
||||
031: vehicle
|
||||
Binary file not shown.
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["钓鱼","游泳"],
|
||||
"labelIndexs":["SL01","SL02"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,61 @@
|
|||
GPUID: 0
|
||||
WORKERS: 1
|
||||
PRINT_FREQ: 10
|
||||
SAVE_FREQ: 10
|
||||
PIN_MEMORY: False
|
||||
OUTPUT_DIR: 'output'
|
||||
|
||||
CUDNN:
|
||||
BENCHMARK: True
|
||||
DETERMINISTIC: False
|
||||
ENABLED: True
|
||||
|
||||
DATASET:
|
||||
DATASET: 360CC
|
||||
ROOT: "../textGenerator/dataset/dataset9/images"
|
||||
CHAR_FILE: '../textGenerator/dataset/dataset9/chars.txt'
|
||||
JSON_FILE: {'train': '../textGenerator/dataset/dataset9/train.txt', 'val': '../textGenerator/dataset/dataset9/val.txt'}
|
||||
# JSON_FILE: {'train': 'H:/DL-DATASET/360M/train.txt', 'val': 'H:/DL-DATASET/360M/test.txt'}
|
||||
SCALE_FACTOR: 0.25
|
||||
ROT_FACTOR: 30
|
||||
STD: 0.193
|
||||
MEAN: 0.588
|
||||
ALPHABETS: ''
|
||||
|
||||
TRAIN:
|
||||
BATCH_SIZE_PER_GPU: 32
|
||||
SHUFFLE: True
|
||||
BEGIN_EPOCH: 0
|
||||
END_EPOCH: 100
|
||||
RESUME:
|
||||
IS_RESUME: False
|
||||
FILE: 'output/360CC/crnn/2023-04-27-13-01/checkpoints/checkpoint_99_acc_0.5030.pth'
|
||||
OPTIMIZER: 'adam'
|
||||
LR: 0.0001
|
||||
WD: 0.0
|
||||
LR_STEP: [60, 80]
|
||||
LR_FACTOR: 0.1
|
||||
MOMENTUM: 0.0
|
||||
NESTEROV: False
|
||||
RMSPROP_ALPHA:
|
||||
RMSPROP_CENTERED:
|
||||
FINETUNE:
|
||||
IS_FINETUNE: False
|
||||
FINETUNE_CHECKPOINIT: 'output/checkpoints/mixed_second_finetune_acc_97P7.pth'
|
||||
FREEZE: true
|
||||
|
||||
TEST:
|
||||
BATCH_SIZE_PER_GPU: 16
|
||||
SHUFFLE: True # for random test rather than test on the whole validation set
|
||||
NUM_TEST_BATCH: 1000
|
||||
NUM_TEST_DISP: 10
|
||||
|
||||
MODEL:
|
||||
NAME: 'crnn'
|
||||
IMAGE_SIZE:
|
||||
OW: 160 # origial width: 280
|
||||
H: 32
|
||||
W: 160 # resized width: 160
|
||||
NUM_CLASSES: 0
|
||||
NUM_HIDDEN: 256
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,92 @@
|
|||
a
|
||||
b
|
||||
c
|
||||
d
|
||||
e
|
||||
f
|
||||
g
|
||||
h
|
||||
i
|
||||
j
|
||||
k
|
||||
l
|
||||
m
|
||||
n
|
||||
o
|
||||
p
|
||||
q
|
||||
r
|
||||
s
|
||||
t
|
||||
u
|
||||
v
|
||||
w
|
||||
x
|
||||
y
|
||||
z
|
||||
A
|
||||
B
|
||||
C
|
||||
D
|
||||
E
|
||||
F
|
||||
G
|
||||
H
|
||||
I
|
||||
J
|
||||
K
|
||||
L
|
||||
M
|
||||
N
|
||||
O
|
||||
P
|
||||
Q
|
||||
R
|
||||
S
|
||||
T
|
||||
U
|
||||
V
|
||||
W
|
||||
X
|
||||
Y
|
||||
Z
|
||||
0
|
||||
1
|
||||
2
|
||||
3
|
||||
4
|
||||
5
|
||||
6
|
||||
7
|
||||
8
|
||||
9
|
||||
°
|
||||
!
|
||||
"
|
||||
#
|
||||
$
|
||||
%
|
||||
&
|
||||
'
|
||||
(
|
||||
)
|
||||
*
|
||||
+
|
||||
,
|
||||
-
|
||||
.
|
||||
/
|
||||
:
|
||||
;
|
||||
?
|
||||
@
|
||||
[
|
||||
\
|
||||
]
|
||||
^
|
||||
_
|
||||
`
|
||||
{
|
||||
|
|
||||
}
|
||||
~
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"labelnames_实际":["国旗","浮标","船名","船只","未挂国旗船只" ],
|
||||
"labelnames":[ "国旗","浮标","船名","船只","未挂国旗船只" ],
|
||||
"labelIndexs":["SL040", "SL041","SL042","SL043","SL044"]
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["人"],
|
||||
"labelIndexs":["SL031"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
#检测混合模型
|
||||
for bus in highWay2 river2 drowning noParking river
|
||||
do
|
||||
diff /mnt/thsw2/DSP2/weights/${bus}/yolov5.pt ${bus}/yolov5.pt
|
||||
diff /mnt/thsw2/DSP2/weights/${bus}/stdc_360X640.pth ${bus}/stdc_360X640.pth
|
||||
done
|
||||
#检查检测模型
|
||||
for bus in forest2 vehicle pedestrian smogfire AnglerSwimmer countryRoad cityMangement
|
||||
do
|
||||
diff /mnt/thsw2/DSP2/weights/${bus}/yolov5.pt ${bus}/yolov5.pt
|
||||
done
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["车辆","垃圾","商贩"],
|
||||
"labelIndexs":["SL01","SL02","SL03"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["车辆","垃圾","商贩","违停"],
|
||||
"labelIndexs":["SL01","SL02","SL03","SL04"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"score_byClass":{"0":0.88,"1":0.3,"2":0.3,"3":0.3 } ,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["车辆","垃圾","商贩","违停"],
|
||||
"labelIndexs":["SL01","SL02","SL03","SL04"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"score_byClass":{"0":0.88,"1":0.3,"2":0.3,"3":0.3 } ,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"labelnames":["护栏","交通标志","非交通标志","施工","施工"],
|
||||
"labelIndexs":["SL01","SL02","SL03","SL04","SL05"],
|
||||
"labelnamesActual":["护栏","交通标志","非交通标志","锥桶","水马" ]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["违法种植"],
|
||||
"labelIndexs":["SL01"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":[ "纵向裂缝","横向裂缝","网状裂缝" ],
|
||||
"labelIndexs":["SL01","SL02","SL03"]
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
crack_yolov5_202302.pt对应类别['pedestrian', 'vehicle', 'D00', 'D10', 'Repair', 'D20', 'D40', 'Block crack', 'JiShui']
|
||||
roaddamage20231028.pt对应类别[ 'D00','D10','D20','D40','D44','D50','Repair','D43','D01','D11','D0w0','Block crack' ]
|
||||
[ 'D00'：纵向裂缝,
|
||||
'D10'：横向裂缝,
|
||||
'D20'：网状裂缝 ]
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["人头"],
|
||||
"labelIndexs":["SL01"]
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
|
||||
"labelnames":[ "人头","人","船只" ],
|
||||
"labelIndexs":[ "SL001","SL002","SL003" ]
|
||||
}
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{
|
||||
"name":"post_process","conf_thres":0.25,"iou_thres":0.25,"classes":9,
|
||||
"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,0],[255,255,0],[255,0,0],[255,0,127],[255,0,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]]
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["林斑","病死树"],
|
||||
"labelIndexs":["SL031","SL032"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["林斑","病死树","行人","火焰","烟雾","云朵"],
|
||||
"labelIndexs":["SL031","SL032","SL033","SL034","SL035","SL036","SL037"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["林斑","病死树","行人","火焰","烟雾","人群"],
|
||||
"labelIndexs":["SL031","SL032","SL033","SL034","SL035","SL036","SL037"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
name,r,g,b,cls
|
||||
0,0,0,0,bg
|
||||
1,128,0,0,road
|
||||
2,0,128,0,vehicle
|
||||
|
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
|
||||
"labelnames":["行人","车辆","裂缝","裂缝","修补","裂缝","坑槽","裂缝","积水", "影子","事故"],
|
||||
"labelnames_实际":["行人","车辆","纵向裂缝","横向裂缝","修补","网状裂纹","坑槽","块状裂纹","积水","影子","事故"],
|
||||
"labelIndexs":["SL01","SL02","SL03","SL04","SL05","SL06","SL007","SL008","SL009","SL010","SL011" ]
|
||||
}
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
|
||||
"labelnames":["车辆"],
|
||||
"labelnames_实际":["车辆"],
|
||||
"labelIndexs":["SL02" ]
|
||||
}
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{
|
||||
"name":"post_process","conf_thres":0.25,"iou_thres":0.25,"classes":9,
|
||||
"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,0],[255,255,0],[255,0,0],[255,0,127],[255,0,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]]
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
|
||||
"labelnames":[ "车","T角点","L角点","违停" ],
|
||||
"labelIndexs":[ "SL001","SL002","SL003","SL004" ]
|
||||
}
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{
|
||||
"name":"post_process","conf_thres":0.25,"iou_thres":0.25,"classes":9,
|
||||
"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,0],[255,255,0],[255,0,0],[255,0,127],[255,0,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]]
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,5 @@
|
|||
name,cls
|
||||
背景,0
|
||||
道路,1
|
||||
车道线,2
|
||||
车辆,3
|
||||
|
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
|
||||
"labelnames":[ "车辆","违停" ],
|
||||
"labelIndexs":[ "SL001","SL002" ]
|
||||
}
|
||||
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{
|
||||
"name":"post_process","conf_thres":0.25,"iou_thres":0.25,"classes":9,
|
||||
"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,0],[255,255,0],[255,0,0],[255,0,127],[255,0,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]]
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,61 @@
|
|||
GPUID: 0
|
||||
WORKERS: 1
|
||||
PRINT_FREQ: 10
|
||||
SAVE_FREQ: 10
|
||||
PIN_MEMORY: False
|
||||
OUTPUT_DIR: 'output'
|
||||
|
||||
CUDNN:
|
||||
BENCHMARK: True
|
||||
DETERMINISTIC: False
|
||||
ENABLED: True
|
||||
|
||||
DATASET:
|
||||
DATASET: 360CC
|
||||
ROOT: "../textGenerator/dataset/dataset9/images"
|
||||
CHAR_FILE: '../textGenerator/dataset/dataset9/chars.txt'
|
||||
JSON_FILE: {'train': '../textGenerator/dataset/dataset9/train.txt', 'val': '../textGenerator/dataset/dataset9/val.txt'}
|
||||
# JSON_FILE: {'train': 'H:/DL-DATASET/360M/train.txt', 'val': 'H:/DL-DATASET/360M/test.txt'}
|
||||
SCALE_FACTOR: 0.25
|
||||
ROT_FACTOR: 30
|
||||
STD: 0.193
|
||||
MEAN: 0.588
|
||||
ALPHABETS: ''
|
||||
|
||||
TRAIN:
|
||||
BATCH_SIZE_PER_GPU: 32
|
||||
SHUFFLE: True
|
||||
BEGIN_EPOCH: 0
|
||||
END_EPOCH: 100
|
||||
RESUME:
|
||||
IS_RESUME: False
|
||||
FILE: 'output/360CC/crnn/2023-04-27-13-01/checkpoints/checkpoint_99_acc_0.5030.pth'
|
||||
OPTIMIZER: 'adam'
|
||||
LR: 0.0001
|
||||
WD: 0.0
|
||||
LR_STEP: [60, 80]
|
||||
LR_FACTOR: 0.1
|
||||
MOMENTUM: 0.0
|
||||
NESTEROV: False
|
||||
RMSPROP_ALPHA:
|
||||
RMSPROP_CENTERED:
|
||||
FINETUNE:
|
||||
IS_FINETUNE: False
|
||||
FINETUNE_CHECKPOINIT: 'output/checkpoints/mixed_second_finetune_acc_97P7.pth'
|
||||
FREEZE: true
|
||||
|
||||
TEST:
|
||||
BATCH_SIZE_PER_GPU: 16
|
||||
SHUFFLE: True # for random test rather than test on the whole validation set
|
||||
NUM_TEST_BATCH: 1000
|
||||
NUM_TEST_DISP: 10
|
||||
|
||||
MODEL:
|
||||
NAME: 'crnn'
|
||||
IMAGE_SIZE:
|
||||
OW: 160 # origial width: 280
|
||||
H: 32
|
||||
W: 160 # resized width: 160
|
||||
NUM_CLASSES: 0
|
||||
NUM_HIDDEN: 256
|
||||
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,92 @@
|
|||
a
|
||||
b
|
||||
c
|
||||
d
|
||||
e
|
||||
f
|
||||
g
|
||||
h
|
||||
i
|
||||
j
|
||||
k
|
||||
l
|
||||
m
|
||||
n
|
||||
o
|
||||
p
|
||||
q
|
||||
r
|
||||
s
|
||||
t
|
||||
u
|
||||
v
|
||||
w
|
||||
x
|
||||
y
|
||||
z
|
||||
A
|
||||
B
|
||||
C
|
||||
D
|
||||
E
|
||||
F
|
||||
G
|
||||
H
|
||||
I
|
||||
J
|
||||
K
|
||||
L
|
||||
M
|
||||
N
|
||||
O
|
||||
P
|
||||
Q
|
||||
R
|
||||
S
|
||||
T
|
||||
U
|
||||
V
|
||||
W
|
||||
X
|
||||
Y
|
||||
Z
|
||||
0
|
||||
1
|
||||
2
|
||||
3
|
||||
4
|
||||
5
|
||||
6
|
||||
7
|
||||
8
|
||||
9
|
||||
°
|
||||
!
|
||||
"
|
||||
#
|
||||
$
|
||||
%
|
||||
&
|
||||
'
|
||||
(
|
||||
)
|
||||
*
|
||||
+
|
||||
,
|
||||
-
|
||||
.
|
||||
/
|
||||
:
|
||||
;
|
||||
?
|
||||
@
|
||||
[
|
||||
\
|
||||
]
|
||||
^
|
||||
_
|
||||
`
|
||||
{
|
||||
|
|
||||
}
|
||||
~
|
||||
|
|
@ -0,0 +1 @@
|
|||
abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789°!"#$%&'()*+,-./:;?@[\]^_`{|}~
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1 @@
|
|||
0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ €ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
|
||||
|
Can't render this file because it contains an unexpected character in line 1 and column 12.
|
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["行人"],
|
||||
"labelIndexs":["SL01"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
|
||||
"labelnames":["坑槽"],
|
||||
"labelnames_实际":["坑槽"],
|
||||
"labelIndexs":["SL01" ]
|
||||
}
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{
|
||||
"name":"post_process","conf_thres":0.25,"iou_thres":0.25,"classes":9,
|
||||
"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,0],[255,255,0],[255,0,0],[255,0,127],[255,0,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]]
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"labelnames":["排口","水生植被","其它","漂浮物","污染排口","菜地","违建","岸坡垃圾"],
|
||||
"labelIndexs":["SL04","SL011","SL013","SL001","SL001","SL002","SL003","SL004" ],
|
||||
"labelOrders":[0,1,2,3,4,5,6,7]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"labelnames":[ "漂浮物","垃圾","排口","非法建筑","非法种植","水生植物","游泳人员","钓鱼人员","船只","蓝藻"] ,
|
||||
"labelIndexs":[ "SL04","SL05","SL06","SL07","SL08","SL09","SL10","SL11","SL12","SL13" ],
|
||||
"labelOrders":[0,1,2,3,4,5,6,7,8,9]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.3,"ovlap_thres_crossCategory":0.65,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"labelnames_实际":["纵向裂缝","横向裂缝","修补","网状裂纹","坑槽","块状裂纹","积水"],
|
||||
"labelnames":["裂缝","裂缝","修补","裂缝","坑槽","裂缝","积水"],
|
||||
"labelIndexs":["SL030","SL031","SL032","SL033","SL034","SL035","SL036"]
|
||||
}
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,
|
||||
"rainbows":[[0,0,255],[0,0,255],[255,0,0],[0,0,255],[255,255,0],[0,0,255],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]]
|
||||
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["船只"],
|
||||
"labelIndexs":["SL01"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
name,r,g,b,cls
|
||||
0,0,0,0,bg
|
||||
1,128,0,0,road
|
||||
2,0,128,0,vehicle
|
||||
|
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"labelnames_实际":[ "0","1","2","3","4","5","6","7","8","9","10","11","12","13","boat" ],
|
||||
"labelnames":[ "0","1","2","3","4","5","6","7","8","9","10","11","12","13","船只" ],
|
||||
"labelIndexs":["SL050", "SL051", "SL052", "SL053", "SL054", "SL055", "SL056", "SL057", "SL058", "SL059", "SL060", "SL061", "SL062","SL063", "SL064" ]
|
||||
}
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,
|
||||
"rainbows":[[0,0,255],[0,0,255],[255,0,0],[0,0,255],[255,255,0],[0,0,255],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]]
|
||||
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["火焰","烟雾"],
|
||||
"labelIndexs":["SL01","SL02"]
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["烟雾","火焰"],
|
||||
"labelIndexs":["SL01","SL02"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
name,r,g,b,cls
|
||||
0,0,0,0,bg
|
||||
1,128,0,0,road
|
||||
2,0,128,0,vehicle
|
||||
|
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"labelnames_实际":["事故"],
|
||||
"labelnames":["事故"],
|
||||
"labelIndexs":["SL040"]
|
||||
}
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,
|
||||
"rainbows":[[0,0,255],[0,0,255],[255,0,0],[0,0,255],[255,255,0],[0,0,255],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]]
|
||||
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
{
|
||||
"labelnames":["车辆"],
|
||||
"labelIndexs":["SL01"]
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
|
||||
|
||||
"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
|
||||
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,201 @@
|
|||
from minio import Minio
|
||||
from minio.error import S3Error
|
||||
import os
|
||||
import paramiko
|
||||
from scp import SCPClient
|
||||
import getpass
|
||||
import yaml
|
||||
|
||||
|
||||
|
||||
|
||||
model_dict = {
|
||||
"001": "AnglerSwimmer",
|
||||
"002": "channel2",
|
||||
"003": "channelEmergency",
|
||||
"004": "cityMangement",
|
||||
"005": "cityMangement2",
|
||||
"006": "cityMangement3",
|
||||
"007": "cityRoad",
|
||||
"008": "conf",
|
||||
"009": "countryRoad",
|
||||
"010": "crackMeasurement",
|
||||
"011": "crowdCounting",
|
||||
"012": "drowning",
|
||||
"013": "forest",
|
||||
"014": "forest2",
|
||||
"015": "forestCrowd",
|
||||
"016": "highWay2",
|
||||
"017": "illParking",
|
||||
"018": "noParking",
|
||||
"019": "ocr2",
|
||||
"020": "ocr_en",
|
||||
"021": "pedestrian",
|
||||
"022": "pothole",
|
||||
"023": "river",
|
||||
"024": "river2",
|
||||
"025": "riverT",
|
||||
"026": "road",
|
||||
"027": "ship",
|
||||
"028": "ship2",
|
||||
"029": "smogfire",
|
||||
"030": "trafficAccident",
|
||||
"031": "vehicle"
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def download_minio_files(endpoint, access_key, secret_key, bucket_name, prefix='', local_dir='downloads'):
|
||||
"""
|
||||
下载MinIO中指定bucket和目录下的所有文件到本地
|
||||
|
||||
Args:
|
||||
endpoint (str): MinIO服务器地址
|
||||
access_key (str): MinIO访问密钥
|
||||
secret_key (str): MinIO密钥
|
||||
bucket_name (str): 要查询的bucket名称
|
||||
prefix (str): 要查询的目录前缀
|
||||
local_dir (str): 本地存储目录
|
||||
"""
|
||||
try:
|
||||
# 创建MinIO客户端
|
||||
client = Minio(
|
||||
endpoint,
|
||||
access_key=access_key,
|
||||
secret_key=secret_key,
|
||||
secure=False
|
||||
)
|
||||
|
||||
# 确保本地目录存在
|
||||
os.makedirs(local_dir, exist_ok=True)
|
||||
|
||||
# 列出对象
|
||||
objects = client.list_objects(bucket_name, prefix=prefix, recursive=True)
|
||||
|
||||
print(f"Downloading files from bucket '{bucket_name}' with prefix '{prefix}':")
|
||||
print("-" * 50)
|
||||
|
||||
# 遍历并下载所有对象
|
||||
for obj in objects:
|
||||
# 构建本地文件路径
|
||||
local_path = os.path.join(local_dir, os.path.relpath(obj.object_name, prefix))
|
||||
|
||||
# 确保目录存在
|
||||
os.makedirs(os.path.dirname(local_path), exist_ok=True)
|
||||
|
||||
# 下载文件
|
||||
client.fget_object(bucket_name, obj.object_name, local_path)
|
||||
|
||||
print(f"Downloaded: {obj.object_name} -> {local_path}")
|
||||
print(f"Size: {obj.size} bytes")
|
||||
print(f"Last Modified: {obj.last_modified}")
|
||||
print("-" * 50)
|
||||
|
||||
return local_dir # 返回下载的本地目录
|
||||
|
||||
except S3Error as err:
|
||||
print(f"MinIO Error occurred: {err}")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
return None
|
||||
|
||||
def deploy_to_remote(local_path, remote_path, host, username, password):
|
||||
"""
|
||||
部署文件到远程服务器
|
||||
|
||||
Args:
|
||||
local_path (str): 本地文件路径
|
||||
remote_path (str): 远程服务器路径
|
||||
host (str): 远程服务器IP
|
||||
username (str): 用户名
|
||||
password (str): 密码
|
||||
"""
|
||||
try:
|
||||
# 创建SSH客户端
|
||||
ssh = paramiko.SSHClient()
|
||||
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
|
||||
ssh.connect(host, username=username, password=password)
|
||||
|
||||
# 创建SCP客户端
|
||||
scp = SCPClient(ssh.get_transport())
|
||||
|
||||
# 上传文件
|
||||
scp.put(local_path, remote_path)
|
||||
print(f"Successfully deployed {local_path} to {remote_path}")
|
||||
|
||||
# 关闭连接
|
||||
scp.close()
|
||||
ssh.close()
|
||||
|
||||
except Exception as e:
|
||||
print(f"Deployment failed: {e}")
|
||||
|
||||
|
||||
|
||||
# 获取 code 对应的模型名称
|
||||
def get_model_name(code):
|
||||
# 确保 code 是3位数字字符串
|
||||
code_str = f"{int(code):03d}" if str(code).isdigit() else code
|
||||
return model_dict.get(code_str, f"Unknown code: {code}")
|
||||
|
||||
|
||||
# if __name__ == "__main__":
|
||||
# 配置MinIO信息
|
||||
ENDPOINT = "minio.t-aaron.com:9000"
|
||||
ACCESS_KEY = "IKf3A0ZSXsR1m0oalMjV"
|
||||
SECRET_KEY = "yoC6qRo2hlyZu8Pdbt6eh9TVaTV4gD7KRudromrk"
|
||||
BUCKET_NAME = "algorithm"
|
||||
PREFIX = "weights/026/v1.0/"
|
||||
LOCAL_DIR = "downloaded_weights" # 本地存储目录
|
||||
|
||||
# 配置远程服务器信息
|
||||
REMOTE_HOST = "192.168.10.66"
|
||||
REMOTE_USER = "thsw2"
|
||||
REMOTE_PASSWORD = "th@2025"
|
||||
REMOTE_PATH = "/home/thsw2/jcq/test_package/AIlib2_test/weights/"
|
||||
|
||||
code = "026"
|
||||
model_yaml = "/home/thsw/WJ/jcq/yolov5_servers/AI_web/api_task/config/Model_config.yaml"
|
||||
model_name = get_model_name(code)
|
||||
|
||||
DEST_PATH = os.path.join(REMOTE_PATH,model_name)
|
||||
|
||||
|
||||
|
||||
|
||||
# 1. 从MinIO下载文件
|
||||
downloaded_dir = download_minio_files(
|
||||
endpoint=ENDPOINT,
|
||||
access_key=ACCESS_KEY,
|
||||
secret_key=SECRET_KEY,
|
||||
bucket_name=BUCKET_NAME,
|
||||
prefix=PREFIX,
|
||||
local_dir=LOCAL_DIR
|
||||
)
|
||||
|
||||
# 2. 如果下载成功，部署到远程服务器
|
||||
if downloaded_dir:
|
||||
# 获取下载的best.pt文件路径
|
||||
best_pt_path = os.path.join(downloaded_dir, "best.pt")
|
||||
|
||||
if os.path.exists(best_pt_path):
|
||||
print("\nStarting deployment to remote server...")
|
||||
deploy_to_remote(
|
||||
local_path=best_pt_path,
|
||||
remote_path=DEST_PATH,
|
||||
host=REMOTE_HOST,
|
||||
username=REMOTE_USER,
|
||||
password=REMOTE_PASSWORD
|
||||
)
|
||||
os.remove(best_pt_path)
|
||||
else:
|
||||
print(f"Error: {best_pt_path} not found in downloaded files")
|
||||
|
|
@ -0,0 +1 @@
|
|||
scp
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
import os
|
||||
import random
|
||||
import yaml
|
||||
from shutil import copyfile
|
||||
|
||||
def split_dataset(data_dir, train_ratio=0.8):
|
||||
# 获取所有jpg文件
|
||||
jpg_files = [f for f in os.listdir(data_dir) if f.endswith('.jpg')]
|
||||
|
||||
# 检查对应的txt文件是否存在
|
||||
valid_files = []
|
||||
for jpg in jpg_files:
|
||||
txt = jpg.replace('.jpg', '.txt')
|
||||
if os.path.exists(os.path.join(data_dir, txt)):
|
||||
valid_files.append(jpg)
|
||||
|
||||
# 随机打乱
|
||||
random.shuffle(valid_files)
|
||||
|
||||
# 划分训练集和验证集
|
||||
split_idx = int(len(valid_files) * train_ratio)
|
||||
train_files = valid_files[:split_idx]
|
||||
val_files = valid_files[split_idx:]
|
||||
|
||||
return train_files, val_files
|
||||
|
||||
def write_file_list(file_list, output_file, base_dir):
|
||||
with open(output_file, 'w') as f:
|
||||
for file in file_list:
|
||||
f.write(os.path.join(base_dir, file) + '\n')
|
||||
|
||||
def copy_files_to_folders(files, src_dir, dest_dir):
|
||||
"""复制图片和标签文件到目标文件夹"""
|
||||
os.makedirs(dest_dir, exist_ok=True)
|
||||
for file in files:
|
||||
# 复制图片
|
||||
src_img = os.path.join(src_dir, file)
|
||||
dest_img = os.path.join(dest_dir, file)
|
||||
copyfile(src_img, dest_img)
|
||||
|
||||
# 复制对应的标签文件
|
||||
src_txt = os.path.join(src_dir, file.replace('.jpg', '.txt'))
|
||||
dest_txt = os.path.join(dest_dir, file.replace('.jpg', '.txt'))
|
||||
copyfile(src_txt, dest_txt)
|
||||
|
||||
def update_yaml(yaml_file, train_txt, val_txt, nc, names):
|
||||
with open(yaml_file, 'r') as f:
|
||||
data = yaml.safe_load(f)
|
||||
|
||||
data['train'] = train_txt
|
||||
data['val'] = val_txt
|
||||
data['nc'] = nc
|
||||
data['names'] = names
|
||||
|
||||
with open(yaml_file, 'w') as f:
|
||||
yaml.dump(data, f, default_flow_style=False)
|
||||
|
||||
# def main():
|
||||
# # 配置参数
|
||||
# data_dir = "/home/th/jcq/AI_AutoPlat/DATA/TrainDatasets/027123"
|
||||
# output_dir = "/home/th/jcq/AI_AutoPlat/DATA/TrainDatasets/027123_temp"
|
||||
|
||||
# # yaml_file = "/home/thsw/WJ/jcq/yolov5-th/data/road.yaml" # 替换为你的yaml文件路径
|
||||
# yaml_file = "/home/thsw/WJ/jcq/yolov5-th/data/cl.yaml" # 替换为你的yaml文件路径
|
||||
|
||||
# # 确保输出目录存在
|
||||
# os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
# # 创建 train 和 val 文件夹
|
||||
# train_dir = os.path.join(data_dir, "train")
|
||||
# val_dir = os.path.join(data_dir, "val")
|
||||
# os.makedirs(train_dir, exist_ok=True)
|
||||
# os.makedirs(val_dir, exist_ok=True)
|
||||
|
||||
# # 划分数据集
|
||||
# train_files, val_files = split_dataset(data_dir)
|
||||
|
||||
# # 复制文件到 train/ 和 val/ 文件夹
|
||||
# copy_files_to_folders(train_files, data_dir, train_dir)
|
||||
# copy_files_to_folders(val_files, data_dir, val_dir)
|
||||
|
||||
# # 写入训练集和验证集txt文件
|
||||
# train_txt = os.path.join(data_dir, "train.txt")
|
||||
# val_txt = os.path.join(data_dir, "val.txt")
|
||||
|
||||
# write_file_list(train_files, train_txt, train_dir)
|
||||
# write_file_list(val_files, val_txt, val_dir)
|
||||
|
||||
# # 更新yaml文件
|
||||
# nc = 5
|
||||
# names = ['flag', 'buoy', 'shipname', 'ship', 'uncover']
|
||||
# update_yaml(yaml_file, train_txt, val_txt, nc, names)
|
||||
|
||||
# print(f"数据集划分完成，训练集: {len(train_files)} 张，验证集: {len(val_files)} 张")
|
||||
# print(f"训练集列表已写入: {train_txt}")
|
||||
# print(f"验证集列表已写入: {val_txt}")
|
||||
# print(f"YAML文件已更新: {yaml_file}")
|
||||
|
||||
# if __name__ == "__main__":
|
||||
# main()
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
import os
|
||||
import random
|
||||
import yaml
|
||||
from shutil import copyfile
|
||||
|
||||
def split_dataset(data_dir, train_ratio=0.8):
|
||||
# 获取所有支持的图像文件(jpg和png)
|
||||
image_files = [f for f in os.listdir(data_dir)
|
||||
if f.endswith('.jpg') or f.endswith('.png')]
|
||||
|
||||
# 检查对应的txt文件是否存在
|
||||
valid_files = []
|
||||
for img_file in image_files:
|
||||
# 根据图像扩展名确定对应的txt文件名
|
||||
if img_file.endswith('.jpg'):
|
||||
txt = img_file.replace('.jpg', '.txt')
|
||||
else: # .png
|
||||
txt = img_file.replace('.png', '.txt')
|
||||
|
||||
if os.path.exists(os.path.join(data_dir, txt)):
|
||||
valid_files.append(img_file)
|
||||
|
||||
# 随机打乱
|
||||
random.shuffle(valid_files)
|
||||
|
||||
# 划分训练集和验证集
|
||||
split_idx = int(len(valid_files) * train_ratio)
|
||||
train_files = valid_files[:split_idx]
|
||||
val_files = valid_files[split_idx:]
|
||||
|
||||
return train_files, val_files
|
||||
|
||||
def write_file_list(file_list, output_file, base_dir):
|
||||
with open(output_file, 'w') as f:
|
||||
for file in file_list:
|
||||
f.write(os.path.join(base_dir, file) + '\n')
|
||||
|
||||
def copy_files_to_folders(files, src_dir, dest_dir):
|
||||
"""复制图片和标签文件到目标文件夹"""
|
||||
os.makedirs(dest_dir, exist_ok=True)
|
||||
for file in files:
|
||||
# 复制图片
|
||||
src_img = os.path.join(src_dir, file)
|
||||
dest_img = os.path.join(dest_dir, file)
|
||||
copyfile(src_img, dest_img)
|
||||
|
||||
# 动态替换扩展名
|
||||
src_txt = os.path.join(src_dir, file.replace('.jpg', '.txt').replace('.png', '.txt'))
|
||||
dest_txt = os.path.join(dest_dir, file.replace('.jpg', '.txt').replace('.png', '.txt'))
|
||||
copyfile(src_txt, dest_txt)
|
||||
|
||||
def update_yaml(yaml_file, train_txt, val_txt, nc, names):
|
||||
with open(yaml_file, 'r') as f:
|
||||
data = yaml.safe_load(f)
|
||||
|
||||
data['train'] = train_txt
|
||||
data['val'] = val_txt
|
||||
data['nc'] = nc
|
||||
data['names'] = names
|
||||
|
||||
with open(yaml_file, 'w') as f:
|
||||
yaml.dump(data, f, default_flow_style=False)
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
from minio import Minio
|
||||
from minio.error import S3Error
|
||||
import os
|
||||
|
||||
# 配置信息
|
||||
ENDPOINT = "minio-jndsj.t-aaron.com:2443"
|
||||
ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2"
|
||||
SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI"
|
||||
|
||||
# 文件信息
|
||||
BUCKET_NAME = "th-dsp"
|
||||
FILE_PATH = "123/27ffd870-a417-4b65-8ec6-76dd5b2693fe.png"
|
||||
LOCAL_SAVE_PATH = "./downloaded_image.png" # 本地保存路径
|
||||
|
||||
filename = os.path.basename(FILE_PATH) # 提取文件名
|
||||
print(filename) # 输出: 27ffd870-a417-4b65-8ec6-76dd5b2693fe.png
|
||||
|
||||
def download_file():
|
||||
try:
|
||||
# 创建 MinIO 客户端
|
||||
client = Minio(
|
||||
ENDPOINT,
|
||||
access_key=ACCESS_KEY,
|
||||
secret_key=SECRET_KEY,
|
||||
secure=True # 使用 HTTPS
|
||||
)
|
||||
|
||||
# 下载文件
|
||||
print(f"正在下载: {BUCKET_NAME}/{FILE_PATH} -> {LOCAL_SAVE_PATH}")
|
||||
client.fget_object(BUCKET_NAME, FILE_PATH, LOCAL_SAVE_PATH)
|
||||
|
||||
print("文件下载完成!")
|
||||
return True
|
||||
|
||||
except S3Error as e:
|
||||
print(f"MinIO 错误: {e}")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"发生错误: {e}")
|
||||
return False
|
||||
|
||||
if __name__ == "__main__":
|
||||
download_file()
|
||||
|
|
@ -0,0 +1,85 @@
|
|||
from minio import Minio
|
||||
from minio.error import S3Error
|
||||
import os
|
||||
|
||||
def download_minio_files(endpoint, access_key, secret_key, bucket_name, prefix='', local_dir='downloads'):
|
||||
"""
|
||||
下载MinIO中指定bucket和目录下的所有文件到本地
|
||||
|
||||
Args:
|
||||
endpoint (str): MinIO服务器地址
|
||||
access_key (str): MinIO访问密钥
|
||||
secret_key (str): MinIO密钥
|
||||
bucket_name (str): 要查询的bucket名称
|
||||
prefix (str): 要查询的目录前缀
|
||||
local_dir (str): 本地存储目录
|
||||
"""
|
||||
try:
|
||||
# 创建MinIO客户端
|
||||
client = Minio(
|
||||
endpoint,
|
||||
access_key=access_key,
|
||||
secret_key=secret_key,
|
||||
secure=False
|
||||
)
|
||||
|
||||
# 确保本地目录存在
|
||||
os.makedirs(local_dir, exist_ok=True)
|
||||
|
||||
# 列出对象
|
||||
objects = client.list_objects(bucket_name, prefix=prefix, recursive=True)
|
||||
|
||||
print(f"Downloading files from bucket '{bucket_name}' with prefix '{prefix}':")
|
||||
print("-" * 50)
|
||||
|
||||
# 遍历并下载所有对象
|
||||
for obj in objects:
|
||||
# 构建本地文件路径
|
||||
local_path = os.path.join(local_dir, os.path.relpath(obj.object_name, prefix))
|
||||
|
||||
# 确保目录存在
|
||||
os.makedirs(os.path.dirname(local_path), exist_ok=True)
|
||||
|
||||
# 下载文件
|
||||
client.fget_object(bucket_name, obj.object_name, local_path)
|
||||
|
||||
print(f"Downloaded: {obj.object_name} -> {local_path}")
|
||||
print(f"Size: {obj.size} bytes")
|
||||
print(f"Last Modified: {obj.last_modified}")
|
||||
print("-" * 50)
|
||||
|
||||
except S3Error as err:
|
||||
print(f"MinIO Error occurred: {err}")
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 配置信息
|
||||
ENDPOINT = "minio-jndsj.t-aaron.com:2443"
|
||||
ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2"
|
||||
SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI"
|
||||
BUCKET_NAME = "algorithm"
|
||||
PREFIX = "weights/024/V1.0/"
|
||||
LOCAL_DIR = "downloaded_weights" # 本地存储目录
|
||||
|
||||
|
||||
# endpoint: 'minio-jndsj.t-aaron.com:2443'
|
||||
# domain: 'https://minio-jndsj.t-aaron.com:2443'
|
||||
# access_key: 'PJM0c2qlauoXv5TMEHm2'
|
||||
# secret_key: 'Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI'
|
||||
# secure: true
|
||||
# image_bucket: 'image'
|
||||
# video_bucket: 'video'
|
||||
|
||||
|
||||
|
||||
|
||||
# 调用函数下载文件
|
||||
download_minio_files(
|
||||
endpoint=ENDPOINT,
|
||||
access_key=ACCESS_KEY,
|
||||
secret_key=SECRET_KEY,
|
||||
bucket_name=BUCKET_NAME,
|
||||
prefix=PREFIX,
|
||||
local_dir=LOCAL_DIR
|
||||
)
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
import io
|
||||
import json
|
||||
|
||||
import cv2
|
||||
import os
|
||||
import urllib3
|
||||
from minio import Minio
|
||||
|
||||
# MinIO
|
||||
|
||||
|
||||
ENDPOINT = "minio-jndsj.t-aaron.com:2443" # MinIO服务器地址
|
||||
ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2" # 访问密钥
|
||||
SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI" # 密钥
|
||||
|
||||
|
||||
|
||||
# # 创建MinIO客户端
|
||||
# minioClient = Minio(
|
||||
# ENDPOINT,
|
||||
# access_key=ACCESS_KEY,
|
||||
# secret_key=SECRET_KEY,
|
||||
# secure=True # 如果使用HTTPS，设置为True
|
||||
# )
|
||||
|
||||
|
||||
|
||||
|
||||
# 创建MinIO客户端
|
||||
minioClient = Minio(
|
||||
ENDPOINT,
|
||||
access_key=ACCESS_KEY,
|
||||
secret_key=SECRET_KEY
|
||||
)
|
||||
|
||||
|
||||
|
||||
aa = 'yolov5.pt' # 本地文件名
|
||||
code = "027"
|
||||
version = "V1.6"
|
||||
|
||||
# 构建 bucket 名称和对象路径
|
||||
bucket_name = "algorithm" # 根据实际情况可能需要调整
|
||||
weights = "weights"
|
||||
object_path = f"{weights}/{code}/{version}/{aa}" # 在 MinIO 中的存储路径
|
||||
|
||||
try:
|
||||
# 上传文件到 MinIO
|
||||
result = minioClient.fput_object(
|
||||
bucket_name=bucket_name,
|
||||
object_name=object_path, # 目标路径
|
||||
file_path=aa, # 本地文件路径
|
||||
)
|
||||
print("文件上传成功:")
|
||||
print(f"Bucket: {result.bucket_name}")
|
||||
print(f"Object: {result.object_name}")
|
||||
print(f"ETag: {result.etag}")
|
||||
|
||||
# 检查 bucket 是否存在
|
||||
if minioClient.bucket_exists("th-dsp"):
|
||||
print("th-dsp exists")
|
||||
else:
|
||||
print("th-dsp does not exist")
|
||||
|
||||
except Exception as e:
|
||||
print(f"上传失败: {e}")
|
||||
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
import io
|
||||
import json
|
||||
|
||||
import cv2
|
||||
import os
|
||||
import urllib3
|
||||
from minio import Minio
|
||||
|
||||
# MinIO
|
||||
|
||||
|
||||
# ENDPOINT = "minio-jndsj.t-aaron.com:2443" # MinIO服务器地址
|
||||
# ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2" # 访问密钥
|
||||
# SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI" # 密钥
|
||||
|
||||
|
||||
ENDPOINT = "minio.t-aaron.com:9000" # MinIO服务器地址
|
||||
ACCESS_KEY = "IKf3A0ZSXsR1m0oalMjV" # 访问密钥
|
||||
SECRET_KEY = "yoC6qRo2hlyZu8Pdbt6eh9TVaTV4gD7KRudromrk" # 密钥
|
||||
|
||||
|
||||
|
||||
# 创建MinIO客户端
|
||||
minioClient = Minio(
|
||||
ENDPOINT,
|
||||
access_key=ACCESS_KEY,
|
||||
secret_key=SECRET_KEY,
|
||||
secure=False # 如果使用HTTPS，设置为True
|
||||
)
|
||||
|
||||
|
||||
|
||||
aa = 'stdc_360X640.pth' # 本地文件名
|
||||
code = "027"
|
||||
version = "V1.6"
|
||||
|
||||
# 构建 bucket 名称和对象路径
|
||||
bucket_name = "algorithm" # 根据实际情况可能需要调整
|
||||
weights = "weights"
|
||||
object_path = f"{weights}/{code}/{version}/{aa}" # 在 MinIO 中的存储路径
|
||||
|
||||
try:
|
||||
# 上传文件到 MinIO
|
||||
result = minioClient.fput_object(
|
||||
bucket_name=bucket_name,
|
||||
object_name=object_path, # 目标路径
|
||||
file_path=aa, # 本地文件路径
|
||||
)
|
||||
print("文件上传成功:")
|
||||
print(f"Bucket: {result.bucket_name}")
|
||||
print(f"Object: {result.object_name}")
|
||||
print(f"ETag: {result.etag}")
|
||||
|
||||
# 检查 bucket 是否存在
|
||||
if minioClient.bucket_exists("th-dsp"):
|
||||
print("th-dsp exists")
|
||||
else:
|
||||
print("th-dsp does not exist")
|
||||
|
||||
except Exception as e:
|
||||
print(f"上传失败: {e}")
|
||||
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
from minio import Minio
|
||||
from minio.error import S3Error
|
||||
import os
|
||||
|
||||
# 配置信息
|
||||
ENDPOINT = "minio-jndsj.t-aaron.com:2443"
|
||||
ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2"
|
||||
SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI"
|
||||
|
||||
|
||||
|
||||
BUCKET_NAME = "algorithm"
|
||||
FOLDER_PATH = "weights/024/V1.3"
|
||||
LOCAL_SAVE_DIR = "./downloaded_files" # 本地保存目录
|
||||
|
||||
# 创建 MinIO 客户端
|
||||
client = Minio(
|
||||
ENDPOINT,
|
||||
access_key=ACCESS_KEY,
|
||||
secret_key=SECRET_KEY,
|
||||
secure=True # 使用 HTTPS
|
||||
)
|
||||
|
||||
def download_folder():
|
||||
try:
|
||||
# 确保本地目录存在
|
||||
os.makedirs(LOCAL_SAVE_DIR, exist_ok=True)
|
||||
|
||||
# 列出文件夹中的所有对象
|
||||
objects = client.list_objects(BUCKET_NAME, prefix=FOLDER_PATH, recursive=True)
|
||||
|
||||
for obj in objects:
|
||||
# 构建本地文件路径
|
||||
local_path = os.path.join(LOCAL_SAVE_DIR, os.path.relpath(obj.object_name, FOLDER_PATH))
|
||||
|
||||
# 确保子目录存在
|
||||
os.makedirs(os.path.dirname(local_path), exist_ok=True)
|
||||
|
||||
# 下载文件
|
||||
print(f"正在下载: {obj.object_name} -> {local_path}")
|
||||
client.fget_object(BUCKET_NAME, obj.object_name, local_path)
|
||||
|
||||
print("所有文件下载完成!")
|
||||
|
||||
except S3Error as e:
|
||||
print(f"MinIO 错误: {e}")
|
||||
except Exception as e:
|
||||
print(f"发生错误: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
download_folder()
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
import torch
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# 配置参数
|
||||
model_dir = Path('/home/th/jcq/AI_AutoPlat/AI_web_dsj/ultralytics') # 使用Path对象更安全
|
||||
model_path = Path('/home/th/jcq/AI_AutoPlat/yolov5-th/yolov5/yolov5s.pt') # 自定义模型权重路径
|
||||
device = 'cuda:0' if torch.cuda.is_available() else 'cpu' # 自动选择设备
|
||||
|
||||
def load_model_offline():
|
||||
"""离线环境专用模型加载函数"""
|
||||
try:
|
||||
# 1. 验证本地文件是否存在
|
||||
if not model_dir.exists():
|
||||
raise FileNotFoundError(f"YOLOv5目录不存在: {model_dir}")
|
||||
if not model_path.exists():
|
||||
raise FileNotFoundError(f"模型权重文件不存在: {model_path}")
|
||||
|
||||
# 2. 确保本地仓库是完整可用的
|
||||
required_files = ['models', 'utils', 'hubconf.py']
|
||||
for f in required_files:
|
||||
if not (model_dir / f).exists():
|
||||
raise FileNotFoundError(f"YOLOv5仓库不完整，缺失: {f}")
|
||||
|
||||
# 3. 强制使用本地加载（禁用任何网络尝试）
|
||||
os.environ['GITHUB_ASSETS'] = 'off' # 禁用github资源下载
|
||||
torch.hub.set_dir(str(model_dir.parent)) # 设置hub缓存目录为本地
|
||||
|
||||
# 4. 加载模型（完全离线模式）
|
||||
model = torch.hub.load(
|
||||
repo_or_dir=str(model_dir),
|
||||
model='custom',
|
||||
path=str(model_path),
|
||||
source='local',
|
||||
force_reload=False,
|
||||
skip_validation=True,
|
||||
device=device,
|
||||
_verbose=False # 禁用hub的详细输出
|
||||
)
|
||||
|
||||
# 5. 验证模型加载成功
|
||||
if not hasattr(model, 'names'):
|
||||
raise RuntimeError("模型加载异常：缺少关键属性")
|
||||
|
||||
print(f"✅ 离线模型加载成功！设备: {device}")
|
||||
print(f"模型类别: {model.names}")
|
||||
return model
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 加载失败: {type(e).__name__}: {e}")
|
||||
# 详细错误诊断
|
||||
if isinstance(e, ModuleNotFoundError):
|
||||
print("\n⚠️ 可能缺少依赖包，请在联网环境执行：")
|
||||
print(f"pip install -r {model_dir/'requirements.txt'}")
|
||||
elif isinstance(e, RuntimeError) and "CUDA" in str(e):
|
||||
print("\n⚠️ CUDA不可用，正在自动切换到CPU模式...")
|
||||
return load_model_offline_cpu()
|
||||
return None
|
||||
|
||||
def load_model_offline_cpu():
|
||||
"""强制CPU模式重试"""
|
||||
try:
|
||||
model = torch.hub.load(
|
||||
str(model_dir),
|
||||
'custom',
|
||||
path=str(model_path),
|
||||
source='local',
|
||||
device='cpu'
|
||||
)
|
||||
print("✅ 回退到CPU模式加载成功")
|
||||
return model
|
||||
except Exception as e:
|
||||
print(f"❌ CPU模式也加载失败: {e}")
|
||||
return None
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 执行加载
|
||||
model = load_model_offline()
|
||||
|
||||
# 使用示例
|
||||
if model:
|
||||
img = torch.zeros((1, 3, 640, 640)) # 测试张量
|
||||
results = model(img)
|
||||
print(f"推理测试完成！检测结果: {results}")
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
import torch
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import os
|
||||
os.environ['GITHUB_ASSETS'] = 'disabled'
|
||||
os.environ['YOLOv5_OFFLINE'] = 'true'
|
||||
os.environ['ULTRALYTICS_HUB'] = 'off'
|
||||
|
||||
# 强制禁用所有网络连接
|
||||
os.environ['GITHUB_ASSETS'] = 'disabled'
|
||||
os.environ['YOLOv5_OFFLINE'] = 'true'
|
||||
|
||||
# 配置路径
|
||||
model_dir = Path('/home/th/jcq/AI_AutoPlat/AI_web_dsj/ultralytics/yolov5')
|
||||
model_path = Path('/home/th/jcq/AI_AutoPlat/yolov5-th/yolov5/yolov5s.pt') # 自定义模型权重路径
|
||||
device = torch.device('cpu') # 离线环境建议强制使用CPU
|
||||
|
||||
def load_yolov5_offline():
|
||||
try:
|
||||
# 1. 手动添加本地YOLOv5到Python路径
|
||||
import sys
|
||||
sys.path.insert(0, str(model_dir))
|
||||
|
||||
# 2. 直接使用YOLOv5本地代码加载
|
||||
from models.experimental import attempt_load
|
||||
model = attempt_load(
|
||||
weights=model_path,
|
||||
device=device,
|
||||
inplace=True,
|
||||
fuse=True # 融合Conv+BN层提升效率
|
||||
)
|
||||
|
||||
# 3. 验证模型
|
||||
if not hasattr(model, 'names'):
|
||||
raise RuntimeError("模型结构异常")
|
||||
|
||||
print(f"✅ 离线加载成功 | 设备: {device} | 类别数: {len(model.names)}")
|
||||
return model
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 加载失败: {type(e).__name__}: {e}")
|
||||
if "No module named" in str(e):
|
||||
print("\n⚠️ 解决方案:")
|
||||
print(f"1. 确认 {model_dir} 包含完整YOLOv5代码")
|
||||
print(f"2. 检查是否有 __init__.py 文件在 models/ 和 utils/ 目录")
|
||||
return None
|
||||
|
||||
if __name__ == "__main__":
|
||||
model = load_yolov5_offline()
|
||||
if model:
|
||||
# 测试推理
|
||||
img = torch.rand((1, 3, 640, 640)) # 模拟输入图像
|
||||
results = model(img)
|
||||
print(f"推理测试完成！输出形状: {results[0].shape}")
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
cff-version: 1.2.0
|
||||
preferred-citation:
|
||||
type: software
|
||||
message: If you use YOLOv5, please cite it as below.
|
||||
authors:
|
||||
- family-names: Jocher
|
||||
given-names: Glenn
|
||||
orcid: "https://orcid.org/0000-0001-5950-6979"
|
||||
title: "YOLOv5 by Ultralytics"
|
||||
version: 7.0
|
||||
doi: 10.5281/zenodo.3908559
|
||||
date-released: 2020-5-29
|
||||
license: AGPL-3.0
|
||||
url: "https://github.com/ultralytics/yolov5"
|
||||
|
|
@ -0,0 +1,76 @@
|
|||
## Contributing to YOLOv5 🚀
|
||||
|
||||
We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's:
|
||||
|
||||
- Reporting a bug
|
||||
- Discussing the current state of the code
|
||||
- Submitting a fix
|
||||
- Proposing a new feature
|
||||
- Becoming a maintainer
|
||||
|
||||
YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be helping push the frontiers of what's possible in AI 😃!
|
||||
|
||||
## Submitting a Pull Request (PR) 🛠️
|
||||
|
||||
Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
|
||||
|
||||
### 1. Select File to Update
|
||||
|
||||
Select `requirements.txt` to update by clicking on it in GitHub.
|
||||
|
||||
<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
|
||||
|
||||
### 2. Click 'Edit this file'
|
||||
|
||||
The button is in the top-right corner.
|
||||
|
||||
<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
|
||||
|
||||
### 3. Make Changes
|
||||
|
||||
Change the `matplotlib` version from `3.2.2` to `3.3`.
|
||||
|
||||
<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
|
||||
|
||||
### 4. Preview Changes and Submit PR
|
||||
|
||||
Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
|
||||
|
||||
<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
|
||||
|
||||
### PR recommendations
|
||||
|
||||
To allow your work to be integrated as seamlessly as possible, we advise you to:
|
||||
|
||||
- ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
|
||||
|
||||
<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 15" src="https://user-images.githubusercontent.com/26833433/187295893-50ed9f44-b2c9-4138-a614-de69bd1753d7.png"></p>
|
||||
|
||||
- ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**.
|
||||
|
||||
<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 03" src="https://user-images.githubusercontent.com/26833433/187296922-545c5498-f64a-4d8c-8300-5fa764360da6.png"></p>
|
||||
|
||||
- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
|
||||
|
||||
## Submitting a Bug Report 🐛
|
||||
|
||||
If you spot a problem with YOLOv5 please submit a Bug Report!
|
||||
|
||||
For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few short guidelines below to help users provide what we need to get started.
|
||||
|
||||
When asking a question, people will be better able to provide help if you provide **code** that they can easily understand and use to **reproduce** the problem. This is referred to by community members as creating a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/). Your code that reproduces the problem should be:
|
||||
|
||||
- ✅ **Minimal** – Use as little code as possible that still produces the same problem
|
||||
- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
|
||||
- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
|
||||
|
||||
In addition to the above requirements, for [Ultralytics](https://www.ultralytics.com/) to provide assistance your code should be:
|
||||
|
||||
- ✅ **Current** – Verify that your code is up-to-date with the current GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new copy to ensure your problem has not already been resolved by previous commits.
|
||||
- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://www.ultralytics.com/) does not provide support for custom code ⚠️.
|
||||
|
||||
If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better understand and diagnose your problem.
|
||||
|
||||
## License
|
||||
|
||||
By contributing, you agree that your contributions will be licensed under the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/)
|
||||
|
|
@ -0,0 +1,661 @@
|
|||
GNU AFFERO GENERAL PUBLIC LICENSE
|
||||
Version 3, 19 November 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU Affero General Public License is a free, copyleft license for
|
||||
software and other kinds of works, specifically designed to ensure
|
||||
cooperation with the community in the case of network server software.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
our General Public Licenses are intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
them if you wish), that you receive source code or can get it if you
|
||||
want it, that you can change the software or use pieces of it in new
|
||||
free programs, and that you know you can do these things.
|
||||
|
||||
Developers that use our General Public Licenses protect your rights
|
||||
with two steps: (1) assert copyright on the software, and (2) offer
|
||||
you this License which gives you legal permission to copy, distribute
|
||||
and/or modify the software.
|
||||
|
||||
A secondary benefit of defending all users' freedom is that
|
||||
improvements made in alternate versions of the program, if they
|
||||
receive widespread use, become available for other developers to
|
||||
incorporate. Many developers of free software are heartened and
|
||||
encouraged by the resulting cooperation. However, in the case of
|
||||
software used on network servers, this result may fail to come about.
|
||||
The GNU General Public License permits making a modified version and
|
||||
letting the public access it on a server without ever releasing its
|
||||
source code to the public.
|
||||
|
||||
The GNU Affero General Public License is designed specifically to
|
||||
ensure that, in such cases, the modified source code becomes available
|
||||
to the community. It requires the operator of a network server to
|
||||
provide the source code of the modified version running there to the
|
||||
users of that server. Therefore, public use of a modified version, on
|
||||
a publicly accessible server, gives the public access to the source
|
||||
code of the modified version.
|
||||
|
||||
An older license, called the Affero General Public License and
|
||||
published by Affero, was designed to accomplish similar goals. This is
|
||||
a different license, not a version of the Affero GPL, but Affero has
|
||||
released a new version of the Affero GPL which permits relicensing under
|
||||
this license.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
|
||||
|
||||
"This License" refers to version 3 of the GNU Affero General Public License.
|
||||
|
||||
"Copyright" also means copyright-like laws that apply to other kinds of
|
||||
works, such as semiconductor masks.
|
||||
|
||||
"The Program" refers to any copyrightable work licensed under this
|
||||
License. Each licensee is addressed as "you". "Licensees" and
|
||||
"recipients" may be individuals or organizations.
|
||||
|
||||
To "modify" a work means to copy from or adapt all or part of the work
|
||||
in a fashion requiring copyright permission, other than the making of an
|
||||
exact copy. The resulting work is called a "modified version" of the
|
||||
earlier work or a work "based on" the earlier work.
|
||||
|
||||
A "covered work" means either the unmodified Program or a work based
|
||||
on the Program.
|
||||
|
||||
To "propagate" a work means to do anything with it that, without
|
||||
permission, would make you directly or secondarily liable for
|
||||
infringement under applicable copyright law, except executing it on a
|
||||
computer or modifying a private copy. Propagation includes copying,
|
||||
distribution (with or without modification), making available to the
|
||||
public, and in some countries other activities as well.
|
||||
|
||||
To "convey" a work means any kind of propagation that enables other
|
||||
parties to make or receive copies. Mere interaction with a user through
|
||||
a computer network, with no transfer of a copy, is not conveying.
|
||||
|
||||
An interactive user interface displays "Appropriate Legal Notices"
|
||||
to the extent that it includes a convenient and prominently visible
|
||||
feature that (1) displays an appropriate copyright notice, and (2)
|
||||
tells the user that there is no warranty for the work (except to the
|
||||
extent that warranties are provided), that licensees may convey the
|
||||
work under this License, and how to view a copy of this License. If
|
||||
the interface presents a list of user commands or options, such as a
|
||||
menu, a prominent item in the list meets this criterion.
|
||||
|
||||
1. Source Code.
|
||||
|
||||
The "source code" for a work means the preferred form of the work
|
||||
for making modifications to it. "Object code" means any non-source
|
||||
form of a work.
|
||||
|
||||
A "Standard Interface" means an interface that either is an official
|
||||
standard defined by a recognized standards body, or, in the case of
|
||||
interfaces specified for a particular programming language, one that
|
||||
is widely used among developers working in that language.
|
||||
|
||||
The "System Libraries" of an executable work include anything, other
|
||||
than the work as a whole, that (a) is included in the normal form of
|
||||
packaging a Major Component, but which is not part of that Major
|
||||
Component, and (b) serves only to enable use of the work with that
|
||||
Major Component, or to implement a Standard Interface for which an
|
||||
implementation is available to the public in source code form. A
|
||||
"Major Component", in this context, means a major essential component
|
||||
(kernel, window system, and so on) of the specific operating system
|
||||
(if any) on which the executable work runs, or a compiler used to
|
||||
produce the work, or an object code interpreter used to run it.
|
||||
|
||||
The "Corresponding Source" for a work in object code form means all
|
||||
the source code needed to generate, install, and (for an executable
|
||||
work) run the object code and to modify the work, including scripts to
|
||||
control those activities. However, it does not include the work's
|
||||
System Libraries, or general-purpose tools or generally available free
|
||||
programs which are used unmodified in performing those activities but
|
||||
which are not part of the work. For example, Corresponding Source
|
||||
includes interface definition files associated with source files for
|
||||
the work, and the source code for shared libraries and dynamically
|
||||
linked subprograms that the work is specifically designed to require,
|
||||
such as by intimate data communication or control flow between those
|
||||
subprograms and other parts of the work.
|
||||
|
||||
The Corresponding Source need not include anything that users
|
||||
can regenerate automatically from other parts of the Corresponding
|
||||
Source.
|
||||
|
||||
The Corresponding Source for a work in source code form is that
|
||||
same work.
|
||||
|
||||
2. Basic Permissions.
|
||||
|
||||
All rights granted under this License are granted for the term of
|
||||
copyright on the Program, and are irrevocable provided the stated
|
||||
conditions are met. This License explicitly affirms your unlimited
|
||||
permission to run the unmodified Program. The output from running a
|
||||
covered work is covered by this License only if the output, given its
|
||||
content, constitutes a covered work. This License acknowledges your
|
||||
rights of fair use or other equivalent, as provided by copyright law.
|
||||
|
||||
You may make, run and propagate covered works that you do not
|
||||
convey, without conditions so long as your license otherwise remains
|
||||
in force. You may convey covered works to others for the sole purpose
|
||||
of having them make modifications exclusively for you, or provide you
|
||||
with facilities for running those works, provided that you comply with
|
||||
the terms of this License in conveying all material for which you do
|
||||
not control copyright. Those thus making or running the covered works
|
||||
for you must do so exclusively on your behalf, under your direction
|
||||
and control, on terms that prohibit them from making any copies of
|
||||
your copyrighted material outside their relationship with you.
|
||||
|
||||
Conveying under any other circumstances is permitted solely under
|
||||
the conditions stated below. Sublicensing is not allowed; section 10
|
||||
makes it unnecessary.
|
||||
|
||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
||||
|
||||
No covered work shall be deemed part of an effective technological
|
||||
measure under any applicable law fulfilling obligations under article
|
||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||
similar laws prohibiting or restricting circumvention of such
|
||||
measures.
|
||||
|
||||
When you convey a covered work, you waive any legal power to forbid
|
||||
circumvention of technological measures to the extent such circumvention
|
||||
is effected by exercising rights under this License with respect to
|
||||
the covered work, and you disclaim any intention to limit operation or
|
||||
modification of the work as a means of enforcing, against the work's
|
||||
users, your or third parties' legal rights to forbid circumvention of
|
||||
technological measures.
|
||||
|
||||
4. Conveying Verbatim Copies.
|
||||
|
||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
|
||||
appropriately publish on each copy an appropriate copyright notice;
|
||||
keep intact all notices stating that this License and any
|
||||
non-permissive terms added in accord with section 7 apply to the code;
|
||||
keep intact all notices of the absence of any warranty; and give all
|
||||
recipients a copy of this License along with the Program.
|
||||
|
||||
You may charge any price or no price for each copy that you convey,
|
||||
and you may offer support or warranty protection for a fee.
|
||||
|
||||
5. Conveying Modified Source Versions.
|
||||
|
||||
You may convey a work based on the Program, or the modifications to
|
||||
produce it from the Program, in the form of source code under the
|
||||
terms of section 4, provided that you also meet all of these conditions:
|
||||
|
||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
|
||||
|
||||
b) The work must carry prominent notices stating that it is
|
||||
released under this License and any conditions added under section
|
||||
7. This requirement modifies the requirement in section 4 to
|
||||
"keep intact all notices".
|
||||
|
||||
c) You must license the entire work, as a whole, under this
|
||||
License to anyone who comes into possession of a copy. This
|
||||
License will therefore apply, along with any applicable section 7
|
||||
additional terms, to the whole of the work, and all its parts,
|
||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
|
||||
invalidate such permission if you have separately received it.
|
||||
|
||||
d) If the work has interactive user interfaces, each must display
|
||||
Appropriate Legal Notices; however, if the Program has interactive
|
||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
|
||||
|
||||
A compilation of a covered work with other separate and independent
|
||||
works, which are not by their nature extensions of the covered work,
|
||||
and which are not combined with it such as to form a larger program,
|
||||
in or on a volume of a storage or distribution medium, is called an
|
||||
"aggregate" if the compilation and its resulting copyright are not
|
||||
used to limit the access or legal rights of the compilation's users
|
||||
beyond what the individual works permit. Inclusion of a covered work
|
||||
in an aggregate does not cause this License to apply to the other
|
||||
parts of the aggregate.
|
||||
|
||||
6. Conveying Non-Source Forms.
|
||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
of sections 4 and 5, provided that you also convey the
|
||||
machine-readable Corresponding Source under the terms of this License,
|
||||
in one of these ways:
|
||||
|
||||
a) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
|
||||
customarily used for software interchange.
|
||||
|
||||
b) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by a
|
||||
written offer, valid for at least three years and valid for as
|
||||
long as you offer spare parts or customer support for that product
|
||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
product that is covered by this License, on a durable physical
|
||||
medium customarily used for software interchange, for a price no
|
||||
more than your reasonable cost of physically performing this
|
||||
conveying of source, or (2) access to copy the
|
||||
Corresponding Source from a network server at no charge.
|
||||
|
||||
c) Convey individual copies of the object code with a copy of the
|
||||
written offer to provide the Corresponding Source. This
|
||||
alternative is allowed only occasionally and noncommercially, and
|
||||
only if you received the object code with such an offer, in accord
|
||||
with subsection 6b.
|
||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
place (gratis or for a charge), and offer equivalent access to the
|
||||
Corresponding Source in the same way through the same place at no
|
||||
further charge. You need not require recipients to copy the
|
||||
Corresponding Source along with the object code. If the place to
|
||||
copy the object code is a network server, the Corresponding Source
|
||||
may be on a different server (operated by you or a third party)
|
||||
that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
|
||||
Corresponding Source. Regardless of what server hosts the
|
||||
Corresponding Source, you remain obligated to ensure that it is
|
||||
available for as long as needed to satisfy these requirements.
|
||||
|
||||
e) Convey the object code using peer-to-peer transmission, provided
|
||||
you inform other peers where the object code and Corresponding
|
||||
Source of the work are being offered to the general public at no
|
||||
charge under subsection 6d.
|
||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
|
||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
tangible personal property which is normally used for personal, family,
|
||||
or household purposes, or (2) anything designed or sold for incorporation
|
||||
into a dwelling. In determining whether a product is a consumer product,
|
||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||
product received by a particular user, "normally used" refers to a
|
||||
typical or common use of that class of product, regardless of the status
|
||||
of the particular user or of the way in which the particular user
|
||||
actually uses, or expects or is expected to use, the product. A product
|
||||
is a consumer product regardless of whether the product has substantial
|
||||
commercial, industrial or non-consumer uses, unless such uses represent
|
||||
the only significant mode of use of the product.
|
||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
procedures, authorization keys, or other information required to install
|
||||
and execute modified versions of a covered work in that User Product from
|
||||
a modified version of its Corresponding Source. The information must
|
||||
suffice to ensure that the continued functioning of the modified object
|
||||
code is in no case prevented or interfered with solely because
|
||||
modification has been made.
|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
specifically for use in, a User Product, and the conveying occurs as
|
||||
part of a transaction in which the right of possession and use of the
|
||||
User Product is transferred to the recipient in perpetuity or for a
|
||||
fixed term (regardless of how the transaction is characterized), the
|
||||
Corresponding Source conveyed under this section must be accompanied
|
||||
by the Installation Information. But this requirement does not apply
|
||||
if neither you nor any third party retains the ability to install
|
||||
modified object code on the User Product (for example, the work has
|
||||
been installed in ROM).
|
||||
|
||||
The requirement to provide Installation Information does not include a
|
||||
requirement to continue to provide support service, warranty, or updates
|
||||
for a work that has been modified or installed by the recipient, or for
|
||||
the User Product in which it has been modified or installed. Access to a
|
||||
network may be denied when the modification itself materially and
|
||||
adversely affects the operation of the network or violates the rules and
|
||||
protocols for communication across the network.
|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
in accord with this section must be in a format that is publicly
|
||||
documented (and with an implementation available to the public in
|
||||
source code form), and must require no special password or key for
|
||||
unpacking, reading or copying.
|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
License by making exceptions from one or more of its conditions.
|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
be treated as though they were included in this License, to the extent
|
||||
that they are valid under applicable law. If additional permissions
|
||||
apply only to part of the Program, that part may be used separately
|
||||
under those permissions, but the entire Program remains governed by
|
||||
this License without regard to the additional permissions.
|
||||
|
||||
When you convey a copy of a covered work, you may at your option
|
||||
remove any additional permissions from that copy, or from any part of
|
||||
it. (Additional permissions may be written to require their own
|
||||
removal in certain cases when you modify the work.) You may place
|
||||
additional permissions on material, added by you to a covered work,
|
||||
for which you have or can give appropriate copyright permission.
|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
add to a covered work, you may (if authorized by the copyright holders of
|
||||
that material) supplement the terms of this License with terms:
|
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the
|
||||
terms of sections 15 and 16 of this License; or
|
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or
|
||||
author attributions in that material or in the Appropriate Legal
|
||||
Notices displayed by works containing it; or
|
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or
|
||||
requiring that modified versions of such material be marked in
|
||||
reasonable ways as different from the original version; or
|
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or
|
||||
authors of the material; or
|
||||
|
||||
e) Declining to grant rights under trademark law for use of some
|
||||
trade names, trademarks, or service marks; or
|
||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
||||
material by anyone who conveys the material (or modified versions of
|
||||
it) with contractual assumptions of liability to the recipient, for
|
||||
any liability that these contractual assumptions directly impose on
|
||||
those licensors and authors.
|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
restrictions" within the meaning of section 10. If the Program as you
|
||||
received it, or any part of it, contains a notice stating that it is
|
||||
governed by this License along with a term that is a further
|
||||
restriction, you may remove that term. If a license document contains
|
||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
||||
of that license document, provided that the further restriction does
|
||||
not survive such relicensing or conveying.
|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
must place, in the relevant source files, a statement of the
|
||||
additional terms that apply to those files, or a notice indicating
|
||||
where to find the applicable terms.
|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
form of a separately written license, or stated as exceptions;
|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
provided under this License. Any attempt otherwise to propagate or
|
||||
modify it is void, and will automatically terminate your rights under
|
||||
this License (including any patent licenses granted under the third
|
||||
paragraph of section 11).
|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
license from a particular copyright holder is reinstated (a)
|
||||
provisionally, unless and until the copyright holder explicitly and
|
||||
finally terminates your license, and (b) permanently, if the copyright
|
||||
holder fails to notify you of the violation by some reasonable means
|
||||
prior to 60 days after the cessation.
|
||||
|
||||
Moreover, your license from a particular copyright holder is
|
||||
reinstated permanently if the copyright holder notifies you of the
|
||||
violation by some reasonable means, this is the first time you have
|
||||
received notice of violation of this License (for any work) from that
|
||||
copyright holder, and you cure the violation prior to 30 days after
|
||||
your receipt of the notice.
|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
licenses of parties who have received copies or rights from you under
|
||||
this License. If your rights have been terminated and not permanently
|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
material under section 10.
|
||||
|
||||
9. Acceptance Not Required for Having Copies.
|
||||
|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
for enforcing compliance by third parties with this License.
|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Remote Network Interaction; Use with the GNU General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, if you modify the
|
||||
Program, your modified version must prominently offer all users
|
||||
interacting with it remotely through a computer network (if your version
|
||||
supports such interaction) an opportunity to receive the Corresponding
|
||||
Source of your version by providing access to the Corresponding Source
|
||||
from a network server at no charge, through some standard or customary
|
||||
means of facilitating copying of software. This Corresponding Source
|
||||
shall include the Corresponding Source for any work covered by version 3
|
||||
of the GNU General Public License that is incorporated pursuant to the
|
||||
following paragraph.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the work with which it is combined will remain governed by version
|
||||
3 of the GNU General Public License.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU Affero General Public License from time to time. Such new versions
|
||||
will be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU Affero General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU Affero General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU Affero General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||
SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU Affero General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU Affero General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU Affero General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If your software can interact with users remotely through a computer
|
||||
network, you should also make sure that it provides a way for users to
|
||||
get its source. For example, if your program is a web application, its
|
||||
interface could display a "Source" link that leads users to an archive
|
||||
of the code. There are many ways you could offer source, and different
|
||||
solutions will be better for different programs; see section 13 for the
|
||||
specific requirements.
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU AGPL, see
|
||||
<https://www.gnu.org/licenses/>.
|
||||
|
|
@ -0,0 +1,470 @@
|
|||
<div align="center">
|
||||
<p>
|
||||
<a href="https://www.ultralytics.com/events/yolovision" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
|
||||
</p>
|
||||
|
||||
[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
|
||||
|
||||
<div>
|
||||
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
|
||||
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
|
||||
<a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
|
||||
<br>
|
||||
<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
|
||||
</div>
|
||||
<br>
|
||||
|
||||
YOLOv5 🚀 is the world's most loved vision AI, representing <a href="https://www.ultralytics.com/">Ultralytics</a> open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
|
||||
|
||||
We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 <a href="https://docs.ultralytics.com/yolov5/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.com/invite/ultralytics">Discord</a> community for questions and discussions!
|
||||
|
||||
To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
|
||||
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<br>
|
||||
|
||||
## <div align="center">YOLO11 🚀 NEW</div>
|
||||
|
||||
We are excited to unveil the launch of Ultralytics YOLO11 🚀, the latest advancement in our state-of-the-art (SOTA) vision models! Available now at **[GitHub](https://github.com/ultralytics/ultralytics)**, YOLO11 builds on our legacy of speed, precision, and ease of use. Whether you're tackling object detection, image segmentation, or image classification, YOLO11 delivers the performance and versatility needed to excel in diverse applications.
|
||||
|
||||
Get started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources:
|
||||
|
||||
[](https://badge.fury.io/py/ultralytics) [](https://www.pepy.tech/projects/ultralytics)
|
||||
|
||||
```bash
|
||||
pip install ultralytics
|
||||
```
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.ultralytics.com/yolo" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png"></a>
|
||||
</div>
|
||||
|
||||
## <div align="center">Documentation</div>
|
||||
|
||||
See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for full documentation on training, testing and deployment. See below for quickstart examples.
|
||||
|
||||
<details open>
|
||||
<summary>Install</summary>
|
||||
|
||||
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ultralytics/yolov5 # clone
|
||||
cd yolov5
|
||||
pip install -r requirements.txt # install
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Inference</summary>
|
||||
|
||||
YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
# Model
|
||||
model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom
|
||||
|
||||
# Images
|
||||
img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list
|
||||
|
||||
# Inference
|
||||
results = model(img)
|
||||
|
||||
# Results
|
||||
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Inference with detect.py</summary>
|
||||
|
||||
`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
|
||||
|
||||
```bash
|
||||
python detect.py --weights yolov5s.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
screen # screenshot
|
||||
path/ # directory
|
||||
list.txt # list of images
|
||||
list.streams # list of streams
|
||||
'path/*.jpg' # glob
|
||||
'https://youtu.be/LNwODJXcvt4' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Training</summary>
|
||||
|
||||
The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
|
||||
|
||||
```bash
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
|
||||
yolov5s 64
|
||||
yolov5m 40
|
||||
yolov5l 24
|
||||
yolov5x 16
|
||||
```
|
||||
|
||||
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
|
||||
|
||||
</details>
|
||||
|
||||
<details open>
|
||||
<summary>Tutorials</summary>
|
||||
|
||||
- [Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) 🚀 RECOMMENDED
|
||||
- [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/) ☘️
|
||||
- [Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)
|
||||
- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 🌟 NEW
|
||||
- [TFLite, ONNX, CoreML, TensorRT Export](https://docs.ultralytics.com/yolov5/tutorials/model_export/) 🚀
|
||||
- [NVIDIA Jetson platform Deployment](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/) 🌟 NEW
|
||||
- [Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)
|
||||
- [Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)
|
||||
- [Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)
|
||||
- [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
|
||||
- [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)
|
||||
- [Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/) 🌟 NEW
|
||||
- [Ultralytics HUB to train and deploy YOLO](https://www.ultralytics.com/hub) 🚀 RECOMMENDED
|
||||
- [ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)
|
||||
- [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)
|
||||
- [Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/) 🌟 NEW
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Integrations</div>
|
||||
|
||||
Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/), [Comet](https://bit.ly/yolov8-readme-comet), [Roboflow](https://roboflow.com/?ref=ultralytics) and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
|
||||
|
||||
<br>
|
||||
<a href="https://www.ultralytics.com/hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
|
||||
<br>
|
||||
<br>
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.ultralytics.com/hub">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://docs.wandb.ai/guides/integrations/ultralytics/">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="ClearML logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://bit.ly/yolov8-readme-comet">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://bit.ly/yolov5-neuralmagic">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
|
||||
</div>
|
||||
|
||||
| Ultralytics HUB 🚀 | W&B | Comet ⭐ NEW | Neural Magic |
|
||||
| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
|
||||
| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://www.ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLOv5 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
|
||||
|
||||
## <div align="center">Ultralytics HUB</div>
|
||||
|
||||
Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** now!
|
||||
|
||||
<a align="center" href="https://www.ultralytics.com/hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
|
||||
|
||||
## <div align="center">Why YOLOv5</div>
|
||||
|
||||
YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
|
||||
<details>
|
||||
<summary>YOLOv5-P5 640 Figure</summary>
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
|
||||
</details>
|
||||
<details>
|
||||
<summary>Figure Notes</summary>
|
||||
|
||||
- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
|
||||
- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
|
||||
- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
|
||||
- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
|
||||
|
||||
</details>
|
||||
|
||||
### Pretrained Checkpoints
|
||||
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
|
||||
| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
|
||||
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
|
||||
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
|
||||
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
|
||||
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
|
||||
| | | | | | | | | |
|
||||
| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
|
||||
| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
|
||||
| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
|
||||
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
|
||||
| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
|
||||
|
||||
<details>
|
||||
<summary>Table Notes</summary>
|
||||
|
||||
- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
|
||||
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
|
||||
- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
|
||||
- **TTA** [Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Segmentation</div>
|
||||
|
||||
Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials.
|
||||
|
||||
<details>
|
||||
<summary>Segmentation Checkpoints</summary>
|
||||
|
||||
<div align="center">
|
||||
<a align="center" href="https://www.ultralytics.com/yolo" target="_blank">
|
||||
<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a>
|
||||
</div>
|
||||
|
||||
We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy reproducibility.
|
||||
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Train time<br><sup>300 epochs<br>A100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TRT A100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |
|
||||
| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
|
||||
| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
|
||||
| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
|
||||
| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
|
||||
| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
|
||||
|
||||
- All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official
|
||||
- **Accuracy** values are for single-model single-scale on COCO dataset.<br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
|
||||
- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). <br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
|
||||
- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Segmentation Usage Examples <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
|
||||
|
||||
### Train
|
||||
|
||||
YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`.
|
||||
|
||||
```bash
|
||||
# Single-GPU
|
||||
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
|
||||
|
||||
# Multi-GPU DDP
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### Val
|
||||
|
||||
Validate YOLOv5s-seg mask mAP on COCO dataset:
|
||||
|
||||
```bash
|
||||
bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images)
|
||||
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate
|
||||
```
|
||||
|
||||
### Predict
|
||||
|
||||
Use pretrained YOLOv5m-seg.pt to predict bus.jpg:
|
||||
|
||||
```bash
|
||||
python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
model = torch.hub.load(
|
||||
"ultralytics/yolov5", "custom", "yolov5m-seg.pt"
|
||||
) # load from PyTorch Hub (WARNING: inference not yet supported)
|
||||
```
|
||||
|
||||
|  |  |
|
||||
| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
|
||||
|
||||
### Export
|
||||
|
||||
Export YOLOv5s-seg model to ONNX and TensorRT:
|
||||
|
||||
```bash
|
||||
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Classification</div>
|
||||
|
||||
YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation and deployment! See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) and visit our [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart tutorials.
|
||||
|
||||
<details>
|
||||
<summary>Classification Checkpoints</summary>
|
||||
|
||||
<br>
|
||||
|
||||
We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility.
|
||||
|
||||
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
|
||||
| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |
|
||||
| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
|
||||
| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
|
||||
| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
|
||||
| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
|
||||
| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
|
||||
| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
|
||||
| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
|
||||
| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
|
||||
| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
|
||||
| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
|
||||
| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
|
||||
|
||||
<details>
|
||||
<summary>Table Notes (click to expand)</summary>
|
||||
|
||||
- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
|
||||
- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
|
||||
- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
|
||||
- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
|
||||
|
||||
</details>
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Classification Usage Examples <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
|
||||
|
||||
### Train
|
||||
|
||||
YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.
|
||||
|
||||
```bash
|
||||
# Single-GPU
|
||||
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
|
||||
|
||||
# Multi-GPU DDP
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### Val
|
||||
|
||||
Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
|
||||
|
||||
```bash
|
||||
bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
|
||||
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate
|
||||
```
|
||||
|
||||
### Predict
|
||||
|
||||
Use pretrained YOLOv5s-cls.pt to predict bus.jpg:
|
||||
|
||||
```bash
|
||||
python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt") # load from PyTorch Hub
|
||||
```
|
||||
|
||||
### Export
|
||||
|
||||
Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
|
||||
|
||||
```bash
|
||||
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Environments</div>
|
||||
|
||||
Get started in seconds with our verified environments. Click each icon below for details.
|
||||
|
||||
<div align="center">
|
||||
<a href="https://bit.ly/yolov5-paperspace-notebook">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolov5">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
|
||||
</div>
|
||||
|
||||
## <div align="center">Contribute</div>
|
||||
|
||||
We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out the [YOLOv5 Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
|
||||
|
||||
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
|
||||
|
||||
<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a>
|
||||
|
||||
## <div align="center">License</div>
|
||||
|
||||
Ultralytics offers two licensing options to accommodate diverse use cases:
|
||||
|
||||
- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for more details.
|
||||
- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://www.ultralytics.com/license).
|
||||
|
||||
## <div align="center">Contact</div>
|
||||
|
||||
For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues), and join our [Discord](https://discord.com/invite/ultralytics) community for questions and discussions!
|
||||
|
||||
<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
|
||||
</div>
|
||||
|
||||
[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation
|
||||
|
|
@ -0,0 +1,470 @@
|
|||
<div align="center">
|
||||
<p>
|
||||
<a href="https://www.ultralytics.com/events/yolovision" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
|
||||
</p>
|
||||
|
||||
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|
||||
|
||||
<div>
|
||||
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
|
||||
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
|
||||
<a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
|
||||
<br>
|
||||
<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
|
||||
</div>
|
||||
<br>
|
||||
|
||||
YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表<a href="https://www.ultralytics.com/"> Ultralytics </a>对未来视觉 AI 方法的开源研究，结合在数千小时的研究和开发中积累的经验教训和最佳实践。
|
||||
|
||||
我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 YOLOv5 <a href="https://docs.ultralytics.com/yolov5/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> 上提交问题以获得支持，并加入我们的 <a href="https://discord.com/invite/ultralytics">Discord</a> 社区进行问题和讨论！
|
||||
|
||||
如需申请企业许可，请在 [Ultralytics Licensing](https://www.ultralytics.com/license) 处填写表格
|
||||
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
|
||||
<a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
## <div align="center">YOLO11 🚀 全新发布</div>
|
||||
|
||||
我们很高兴宣布推出 Ultralytics YOLO11 🚀，这是我们最先进视觉模型的最新进展！现已在 **[GitHub](https://github.com/ultralytics/ultralytics)** 上发布。YOLO11 在速度、精度和易用性方面进一步提升，无论是处理目标检测、图像分割还是图像分类任务，YOLO11 都具备出色的性能和多功能性，助您在各种应用中脱颖而出。
|
||||
|
||||
立即开始，解锁 YOLO11 的全部潜力！访问 [Ultralytics 文档](https://docs.ultralytics.com/) 获取全面的指南和资源：
|
||||
|
||||
[](https://badge.fury.io/py/ultralytics) [](https://www.pepy.tech/projects/ultralytics)
|
||||
|
||||
```bash
|
||||
pip install ultralytics
|
||||
```
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.ultralytics.com/yolo" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png"></a>
|
||||
</div>
|
||||
|
||||
## <div align="center">文档</div>
|
||||
|
||||
有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com/yolov5/)。请参阅下面的快速入门示例。
|
||||
|
||||
<details open>
|
||||
<summary>安装</summary>
|
||||
|
||||
克隆 repo，并要求在 [**Python>=3.8.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 。
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ultralytics/yolov5 # clone
|
||||
cd yolov5
|
||||
pip install -r requirements.txt # install
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>推理</summary>
|
||||
|
||||
使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
# Model
|
||||
model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom
|
||||
|
||||
# Images
|
||||
img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list
|
||||
|
||||
# Inference
|
||||
results = model(img)
|
||||
|
||||
# Results
|
||||
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>使用 detect.py 推理</summary>
|
||||
|
||||
`detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。
|
||||
|
||||
```bash
|
||||
python detect.py --weights yolov5s.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
screen # screenshot
|
||||
path/ # directory
|
||||
list.txt # list of images
|
||||
list.streams # list of streams
|
||||
'path/*.jpg' # glob
|
||||
'https://youtu.be/LNwODJXcvt4' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>训练</summary>
|
||||
|
||||
下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data)
|
||||
将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/) 训练速度更快）。 尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。
|
||||
|
||||
```bash
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
|
||||
yolov5s 64
|
||||
yolov5m 40
|
||||
yolov5l 24
|
||||
yolov5x 16
|
||||
```
|
||||
|
||||
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
|
||||
|
||||
</details>
|
||||
|
||||
<details open>
|
||||
<summary>教程</summary>
|
||||
|
||||
- [自定义数据训练](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) 🚀 **推荐**
|
||||
- [最佳训练效果的提示](https://docs.ultralytics.com/guides/model-training-tips/) ☘️
|
||||
- [多GPU训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)
|
||||
- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 🌟 **全新**
|
||||
- [TFLite, ONNX, CoreML, TensorRT 导出](https://docs.ultralytics.com/yolov5/tutorials/model_export/) 🚀
|
||||
- [NVIDIA Jetson 平台部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/) 🌟 **全新**
|
||||
- [测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)
|
||||
- [模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)
|
||||
- [模型剪枝/稀疏化](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)
|
||||
- [超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
|
||||
- [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)
|
||||
- [架构概述](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/) 🌟 **全新**
|
||||
- [使用 Ultralytics HUB 进行 YOLO 训练和部署](https://www.ultralytics.com/hub) 🚀 **推荐**
|
||||
- [ClearML 日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)
|
||||
- [与 Neural Magic 的 Deepsparse 集成的 YOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)
|
||||
- [Comet 日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/) 🌟 **全新**
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">集成</div>
|
||||
|
||||
我们与领先的 AI 平台的关键集成扩展了 Ultralytics 产品的功能，提升了数据集标注、训练、可视化和模型管理等任务。探索 Ultralytics 如何通过与 [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/)、[Comet](https://bit.ly/yolov8-readme-comet)、[Roboflow](https://roboflow.com/?ref=ultralytics) 和 [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 的合作，优化您的 AI 工作流程。
|
||||
|
||||
<br>
|
||||
<a href="https://www.ultralytics.com/hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
|
||||
<br>
|
||||
<br>
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.ultralytics.com/hub">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://docs.wandb.ai/guides/integrations/ultralytics/">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="W&B logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://bit.ly/yolov8-readme-comet">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://bit.ly/yolov5-neuralmagic">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
|
||||
</div>
|
||||
|
||||
| Ultralytics HUB 🚀 | W&B | Comet ⭐ 全新 | Neural Magic |
|
||||
| :----------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: |
|
||||
| 简化 YOLO 工作流程：通过 [Ultralytics HUB](https://www.ultralytics.com/hub) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) 跟踪实验、超参数和结果 | 永久免费，[Comet](https://bit.ly/yolov5-readme-comet) 允许您保存 YOLO11 模型、恢复训练，并交互式地可视化和调试预测结果 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 运行 YOLO11 推理，速度提升至 6 倍 |
|
||||
|
||||
## <div align="center">Ultralytics HUB</div>
|
||||
|
||||
[Ultralytics HUB](https://www.ultralytics.com/hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他！
|
||||
|
||||
<a align="center" href="https://www.ultralytics.com/hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
|
||||
|
||||
## <div align="center">为什么选择 YOLOv5</div>
|
||||
|
||||
YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结果。
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
|
||||
<details>
|
||||
<summary>YOLOv5-P5 640 图</summary>
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
|
||||
</details>
|
||||
<details>
|
||||
<summary>图表笔记</summary>
|
||||
|
||||
- **COCO AP val** 表示 mAP@0.5:0.95 指标，在 [COCO val2017](http://cocodataset.org) 数据集的 5000 张图像上测得， 图像包含 256 到 1536 各种推理大小。
|
||||
- **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间，使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例，batchsize 为 32 。
|
||||
- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ， batchsize 为32。
|
||||
- **复现命令** 为 `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
|
||||
|
||||
</details>
|
||||
|
||||
### 预训练模型
|
||||
|
||||
| 模型 | 尺寸<br><sup>（像素） | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | 推理速度<br><sup>CPU b1<br>（ms） | 推理速度<br><sup>V100 b1<br>（ms） | 速度<br><sup>V100 b32<br>（ms） | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ---------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | --------------------------------- | ---------------------------------- | ------------------------------- | ------------------ | ---------------------- |
|
||||
| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
|
||||
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
|
||||
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
|
||||
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
|
||||
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
|
||||
| | | | | | | | | |
|
||||
| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
|
||||
| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
|
||||
| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
|
||||
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
|
||||
| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+[TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
|
||||
|
||||
<details>
|
||||
<summary>笔记</summary>
|
||||
|
||||
- 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。
|
||||
- \*\*mAP<sup>val</sup>\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。<br>复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
|
||||
- **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。<br>复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1`
|
||||
- **TTA** [测试时数据增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) 包括反射和尺度变换。<br>复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">实例分割模型 ⭐ 新</div>
|
||||
|
||||
我们新的 YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) 实例分割模型是世界上最快和最准确的模型，击败所有当前 [SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco)。我们使它非常易于训练、验证和部署。更多细节请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v7.0) 或访问我们的 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) 以快速入门。
|
||||
|
||||
<details>
|
||||
<summary>实例分割模型列表</summary>
|
||||
|
||||
<br>
|
||||
|
||||
<div align="center">
|
||||
<a align="center" href="https://www.ultralytics.com/yolo" target="_blank">
|
||||
<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a>
|
||||
</div>
|
||||
|
||||
我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现，我们在 Google [Colab Pro](https://colab.research.google.com/signup) 上进行了所有速度测试。
|
||||
|
||||
| 模型 | 尺寸<br><sup>（像素） | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 训练时长<br><sup>300 epochs<br>A100 GPU（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TRT A100<br>（ms） | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | ----------------------------------------------- | ----------------------------------- | ----------------------------------- | ------------------ | ---------------------- |
|
||||
| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
|
||||
| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
|
||||
| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
|
||||
| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
|
||||
| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
|
||||
|
||||
- 所有模型使用 SGD 优化器训练， 都使用 `lr0=0.01` 和 `weight_decay=5e-5` 参数， 图像大小为 640 。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5_v70_official
|
||||
- **准确性**结果都在 COCO 数据集上，使用单模型单尺度测试得到。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
|
||||
- **推理速度**是使用 100 张图像推理时间进行平均得到，测试环境使用 [Colab Pro](https://colab.research.google.com/signup) 上 A100 高 RAM 实例。结果仅表示推理速度（NMS 每张图像增加约 1 毫秒）。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
|
||||
- **模型转换**到 FP32 的 ONNX 和 FP16 的 TensorRT 脚本为 `export.py`.<br>运行命令 `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>分割模型使用示例 <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
|
||||
|
||||
### 训练
|
||||
|
||||
YOLOv5分割训练支持自动下载 COCO128-seg 分割数据集，用户仅需在启动指令中包含 `--data coco128-seg.yaml` 参数。 若要手动下载，使用命令 `bash data/scripts/get_coco.sh --train --val --segments`， 在下载完毕后，使用命令 `python train.py --data coco.yaml` 开启训练。
|
||||
|
||||
```bash
|
||||
# 单 GPU
|
||||
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
|
||||
|
||||
# 多 GPU， DDP 模式
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### 验证
|
||||
|
||||
在 COCO 数据集上验证 YOLOv5s-seg mask mAP：
|
||||
|
||||
```bash
|
||||
bash data/scripts/get_coco.sh --val --segments # 下载 COCO val segments 数据集 (780MB, 5000 images)
|
||||
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验证
|
||||
```
|
||||
|
||||
### 预测
|
||||
|
||||
使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg：
|
||||
|
||||
```bash
|
||||
python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
model = torch.hub.load(
|
||||
"ultralytics/yolov5", "custom", "yolov5m-seg.pt"
|
||||
) # 从load from PyTorch Hub 加载模型 (WARNING: 推理暂未支持)
|
||||
```
|
||||
|
||||
|  |  |
|
||||
| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
|
||||
|
||||
### 模型导出
|
||||
|
||||
将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT：
|
||||
|
||||
```bash
|
||||
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">分类网络 ⭐ 新</div>
|
||||
|
||||
YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对分类模型训练、验证和部署的支持！详情请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v6.2) 或访问我们的 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) 以快速入门。
|
||||
|
||||
<details>
|
||||
<summary>分类网络模型</summary>
|
||||
|
||||
<br>
|
||||
|
||||
我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现，我们在 Google 上进行了所有速度测试 [Colab Pro](https://colab.research.google.com/signup) 。
|
||||
|
||||
| 模型 | 尺寸<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>top5 | 训练时长<br><sup>90 epochs<br>4xA100（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TensorRT V100<br>（ms） | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ----------------------------------- | ---------------------------------------- | ---------------- | ---------------------- |
|
||||
| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
|
||||
| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
|
||||
| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
|
||||
| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
|
||||
| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
|
||||
| [Resnetzch](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
|
||||
| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
|
||||
| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
|
||||
| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
|
||||
| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
|
||||
| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
|
||||
|
||||
<details>
|
||||
<summary>Table Notes (点击以展开)</summary>
|
||||
|
||||
- 所有模型都使用 SGD 优化器训练 90 个 epochs，都使用 `lr0=0.001` 和 `weight_decay=5e-5` 参数， 图像大小为 224 ，且都使用默认设置。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
|
||||
- **准确性**都在单模型单尺度上计算，数据集使用 [ImageNet-1k](https://www.image-net.org/index.php) 。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224`
|
||||
- **推理速度**是使用 100 个推理图像进行平均得到，测试环境使用谷歌 [Colab Pro](https://colab.research.google.com/signup) V100 高 RAM 实例。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
|
||||
- **模型导出**到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 `export.py` 。<br>复现命令 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
|
||||
</details>
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>分类训练示例 <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
|
||||
|
||||
### 训练
|
||||
|
||||
YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集，命令中使用 `--data` 即可。 MNIST 示例 `--data mnist` 。
|
||||
|
||||
```bash
|
||||
# 单 GPU
|
||||
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
|
||||
|
||||
# 多 GPU， DDP 模式
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### 验证
|
||||
|
||||
在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性：
|
||||
|
||||
```bash
|
||||
bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
|
||||
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate
|
||||
```
|
||||
|
||||
### 预测
|
||||
|
||||
使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg：
|
||||
|
||||
```bash
|
||||
python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt") # load from PyTorch Hub
|
||||
```
|
||||
|
||||
### 模型导出
|
||||
|
||||
将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT：
|
||||
|
||||
```bash
|
||||
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">环境</div>
|
||||
|
||||
使用下面我们经过验证的环境，在几秒钟内开始使用 YOLOv5 。单击下面的图标了解详细信息。
|
||||
|
||||
<div align="center">
|
||||
<a href="https://bit.ly/yolov5-paperspace-notebook">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolov5">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
|
||||
</div>
|
||||
|
||||
## <div align="center">贡献</div>
|
||||
|
||||
我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的 [投稿指南](https://docs.ultralytics.com/help/contributing/)，并填写 [YOLOv5调查](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！
|
||||
|
||||
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
|
||||
|
||||
<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a>
|
||||
|
||||
## <div align="center">许可证</div>
|
||||
|
||||
Ultralytics 提供两种许可证选项以适应各种使用场景：
|
||||
|
||||
- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/license)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件以了解更多细节。
|
||||
- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://www.ultralytics.com/license)与我们联系。
|
||||
|
||||
## <div align="center">联系方式</div>
|
||||
|
||||
对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)，并加入我们的 [Discord](https://discord.com/invite/ultralytics) 社区进行问题和讨论！
|
||||
|
||||
<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
|
||||
</div>
|
||||
|
||||
[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation
|
||||
|
|
@ -0,0 +1,294 @@
|
|||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
"""
|
||||
Run YOLOv5 benchmarks on all supported export formats.
|
||||
|
||||
Format | `export.py --include` | Model
|
||||
--- | --- | ---
|
||||
PyTorch | - | yolov5s.pt
|
||||
TorchScript | `torchscript` | yolov5s.torchscript
|
||||
ONNX | `onnx` | yolov5s.onnx
|
||||
OpenVINO | `openvino` | yolov5s_openvino_model/
|
||||
TensorRT | `engine` | yolov5s.engine
|
||||
CoreML | `coreml` | yolov5s.mlpackage
|
||||
TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
|
||||
TensorFlow GraphDef | `pb` | yolov5s.pb
|
||||
TensorFlow Lite | `tflite` | yolov5s.tflite
|
||||
TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
|
||||
TensorFlow.js | `tfjs` | yolov5s_web_model/
|
||||
|
||||
Requirements:
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
|
||||
$ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT
|
||||
|
||||
Usage:
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import platform
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
# ROOT = ROOT.relative_to(Path.cwd()) # relative
|
||||
|
||||
import export
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import SegmentationModel
|
||||
from segment.val import run as val_seg
|
||||
from utils import notebook_init
|
||||
from utils.general import LOGGER, check_yaml, file_size, print_args
|
||||
from utils.torch_utils import select_device
|
||||
from val import run as val_det
|
||||
|
||||
|
||||
def run(
|
||||
weights=ROOT / "yolov5s.pt", # weights path
|
||||
imgsz=640, # inference size (pixels)
|
||||
batch_size=1, # batch size
|
||||
data=ROOT / "data/coco128.yaml", # dataset.yaml path
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
half=False, # use FP16 half-precision inference
|
||||
test=False, # test exports only
|
||||
pt_only=False, # test PyTorch only
|
||||
hard_fail=False, # throw error on benchmark failure
|
||||
):
|
||||
"""
|
||||
Run YOLOv5 benchmarks on multiple export formats and log results for model performance evaluation.
|
||||
|
||||
Args:
|
||||
weights (Path | str): Path to the model weights file (default: ROOT / "yolov5s.pt").
|
||||
imgsz (int): Inference size in pixels (default: 640).
|
||||
batch_size (int): Batch size for inference (default: 1).
|
||||
data (Path | str): Path to the dataset.yaml file (default: ROOT / "data/coco128.yaml").
|
||||
device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu' (default: "").
|
||||
half (bool): Use FP16 half-precision inference (default: False).
|
||||
test (bool): Test export formats only (default: False).
|
||||
pt_only (bool): Test PyTorch format only (default: False).
|
||||
hard_fail (bool): Throw an error on benchmark failure if True (default: False).
|
||||
|
||||
Returns:
|
||||
None. Logs information about the benchmark results, including the format, size, mAP50-95, and inference time.
|
||||
|
||||
Notes:
|
||||
Supported export formats and models include PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML,
|
||||
TensorFlow SavedModel, TensorFlow GraphDef, TensorFlow Lite, and TensorFlow Edge TPU. Edge TPU and TF.js
|
||||
are unsupported.
|
||||
|
||||
Example:
|
||||
```python
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
```
|
||||
|
||||
Usage:
|
||||
Install required packages:
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU support
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU support
|
||||
$ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT
|
||||
|
||||
Run benchmarks:
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
"""
|
||||
y, t = [], time.time()
|
||||
device = select_device(device)
|
||||
model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc.
|
||||
for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU)
|
||||
try:
|
||||
assert i not in (9, 10), "inference not supported" # Edge TPU and TF.js are unsupported
|
||||
assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13" # CoreML
|
||||
if "cpu" in device.type:
|
||||
assert cpu, "inference not supported on CPU"
|
||||
if "cuda" in device.type:
|
||||
assert gpu, "inference not supported on GPU"
|
||||
|
||||
# Export
|
||||
if f == "-":
|
||||
w = weights # PyTorch format
|
||||
else:
|
||||
w = export.run(
|
||||
weights=weights, imgsz=[imgsz], include=[f], batch_size=batch_size, device=device, half=half
|
||||
)[-1] # all others
|
||||
assert suffix in str(w), "export failed"
|
||||
|
||||
# Validate
|
||||
if model_type == SegmentationModel:
|
||||
result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
|
||||
metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
|
||||
else: # DetectionModel:
|
||||
result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
|
||||
metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls))
|
||||
speed = result[2][1] # times (preprocess, inference, postprocess)
|
||||
y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference
|
||||
except Exception as e:
|
||||
if hard_fail:
|
||||
assert type(e) is AssertionError, f"Benchmark --hard-fail for {name}: {e}"
|
||||
LOGGER.warning(f"WARNING ⚠️ Benchmark failure for {name}: {e}")
|
||||
y.append([name, None, None, None]) # mAP, t_inference
|
||||
if pt_only and i == 0:
|
||||
break # break after PyTorch
|
||||
|
||||
# Print results
|
||||
LOGGER.info("\n")
|
||||
parse_opt()
|
||||
notebook_init() # print system info
|
||||
c = ["Format", "Size (MB)", "mAP50-95", "Inference time (ms)"] if map else ["Format", "Export", "", ""]
|
||||
py = pd.DataFrame(y, columns=c)
|
||||
LOGGER.info(f"\nBenchmarks complete ({time.time() - t:.2f}s)")
|
||||
LOGGER.info(str(py if map else py.iloc[:, :2]))
|
||||
if hard_fail and isinstance(hard_fail, str):
|
||||
metrics = py["mAP50-95"].array # values to compare to floor
|
||||
floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
|
||||
assert all(x > floor for x in metrics if pd.notna(x)), f"HARD FAIL: mAP50-95 < floor {floor}"
|
||||
return py
|
||||
|
||||
|
||||
def test(
|
||||
weights=ROOT / "yolov5s.pt", # weights path
|
||||
imgsz=640, # inference size (pixels)
|
||||
batch_size=1, # batch size
|
||||
data=ROOT / "data/coco128.yaml", # dataset.yaml path
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
half=False, # use FP16 half-precision inference
|
||||
test=False, # test exports only
|
||||
pt_only=False, # test PyTorch only
|
||||
hard_fail=False, # throw error on benchmark failure
|
||||
):
|
||||
"""
|
||||
Run YOLOv5 export tests for all supported formats and log the results, including export statuses.
|
||||
|
||||
Args:
|
||||
weights (Path | str): Path to the model weights file (.pt format). Default is 'ROOT / "yolov5s.pt"'.
|
||||
imgsz (int): Inference image size (in pixels). Default is 640.
|
||||
batch_size (int): Batch size for testing. Default is 1.
|
||||
data (Path | str): Path to the dataset configuration file (.yaml format). Default is 'ROOT / "data/coco128.yaml"'.
|
||||
device (str): Device for running the tests, can be 'cpu' or a specific CUDA device ('0', '0,1,2,3', etc.). Default is an empty string.
|
||||
half (bool): Use FP16 half-precision for inference if True. Default is False.
|
||||
test (bool): Test export formats only without running inference. Default is False.
|
||||
pt_only (bool): Test only the PyTorch model if True. Default is False.
|
||||
hard_fail (bool): Raise error on export or test failure if True. Default is False.
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: DataFrame containing the results of the export tests, including format names and export statuses.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
```
|
||||
|
||||
Notes:
|
||||
Supported export formats and models include PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow
|
||||
SavedModel, TensorFlow GraphDef, TensorFlow Lite, and TensorFlow Edge TPU. Edge TPU and TF.js are unsupported.
|
||||
|
||||
Usage:
|
||||
Install required packages:
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU support
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU support
|
||||
$ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT
|
||||
Run export tests:
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
"""
|
||||
y, t = [], time.time()
|
||||
device = select_device(device)
|
||||
for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable)
|
||||
try:
|
||||
w = (
|
||||
weights
|
||||
if f == "-"
|
||||
else export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]
|
||||
) # weights
|
||||
assert suffix in str(w), "export failed"
|
||||
y.append([name, True])
|
||||
except Exception:
|
||||
y.append([name, False]) # mAP, t_inference
|
||||
|
||||
# Print results
|
||||
LOGGER.info("\n")
|
||||
parse_opt()
|
||||
notebook_init() # print system info
|
||||
py = pd.DataFrame(y, columns=["Format", "Export"])
|
||||
LOGGER.info(f"\nExports complete ({time.time() - t:.2f}s)")
|
||||
LOGGER.info(str(py))
|
||||
return py
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""
|
||||
Parses command-line arguments for YOLOv5 model inference configuration.
|
||||
|
||||
Args:
|
||||
weights (str): The path to the weights file. Defaults to 'ROOT / "yolov5s.pt"'.
|
||||
imgsz (int): Inference size in pixels. Defaults to 640.
|
||||
batch_size (int): Batch size. Defaults to 1.
|
||||
data (str): Path to the dataset YAML file. Defaults to 'ROOT / "data/coco128.yaml"'.
|
||||
device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu'. Defaults to an empty string (auto-select).
|
||||
half (bool): Use FP16 half-precision inference. This is a flag and defaults to False.
|
||||
test (bool): Test exports only. This is a flag and defaults to False.
|
||||
pt_only (bool): Test PyTorch only. This is a flag and defaults to False.
|
||||
hard_fail (bool | str): Throw an error on benchmark failure. Can be a boolean or a string representing a minimum
|
||||
metric floor, e.g., '0.29'. Defaults to False.
|
||||
|
||||
Returns:
|
||||
argparse.Namespace: Parsed command-line arguments encapsulated in an argparse Namespace object.
|
||||
|
||||
Notes:
|
||||
The function modifies the 'opt.data' by checking and validating the YAML path using 'check_yaml()'.
|
||||
The parsed arguments are printed for reference using 'print_args()'.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
|
||||
parser.add_argument("--batch-size", type=int, default=1, help="batch size")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--test", action="store_true", help="test exports only")
|
||||
parser.add_argument("--pt-only", action="store_true", help="test PyTorch only")
|
||||
parser.add_argument("--hard-fail", nargs="?", const=True, default=False, help="Exception on error or < min metric")
|
||||
opt = parser.parse_args()
|
||||
opt.data = check_yaml(opt.data) # check YAML
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""
|
||||
Executes YOLOv5 benchmark tests or main training/inference routines based on the provided command-line arguments.
|
||||
|
||||
Args:
|
||||
opt (argparse.Namespace): Parsed command-line arguments including options for weights, image size, batch size, data
|
||||
configuration, device, and other flags for inference settings.
|
||||
|
||||
Returns:
|
||||
None: This function does not return any value. It leverages side-effects such as logging and running benchmarks.
|
||||
|
||||
Example:
|
||||
```python
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
```
|
||||
|
||||
Notes:
|
||||
- For a complete list of supported export formats and their respective requirements, refer to the
|
||||
[Ultralytics YOLOv5 Export Formats](https://github.com/ultralytics/yolov5#export-formats).
|
||||
- Ensure that you have installed all necessary dependencies by following the installation instructions detailed in
|
||||
the [main repository](https://github.com/ultralytics/yolov5#installation).
|
||||
|
||||
```shell
|
||||
# Running benchmarks on default weights and image size
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
```
|
||||
"""
|
||||
test(**vars(opt)) if opt.test else run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
|
|
@ -0,0 +1,241 @@
|
|||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
"""
|
||||
Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
|
||||
|
||||
Usage - sources:
|
||||
$ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
screen # screenshot
|
||||
path/ # directory
|
||||
list.txt # list of images
|
||||
list.streams # list of streams
|
||||
'path/*.jpg' # glob
|
||||
'https://youtu.be/LNwODJXcvt4' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
|
||||
Usage - formats:
|
||||
$ python classify/predict.py --weights yolov5s-cls.pt # PyTorch
|
||||
yolov5s-cls.torchscript # TorchScript
|
||||
yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s-cls_openvino_model # OpenVINO
|
||||
yolov5s-cls.engine # TensorRT
|
||||
yolov5s-cls.mlmodel # CoreML (macOS-only)
|
||||
yolov5s-cls_saved_model # TensorFlow SavedModel
|
||||
yolov5s-cls.pb # TensorFlow GraphDef
|
||||
yolov5s-cls.tflite # TensorFlow Lite
|
||||
yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s-cls_paddle_model # PaddlePaddle
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from ultralytics.utils.plotting import Annotator
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.augmentations import classify_transforms
|
||||
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
Profile,
|
||||
check_file,
|
||||
check_img_size,
|
||||
check_imshow,
|
||||
check_requirements,
|
||||
colorstr,
|
||||
cv2,
|
||||
increment_path,
|
||||
print_args,
|
||||
strip_optimizer,
|
||||
)
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
weights=ROOT / "yolov5s-cls.pt", # model.pt path(s)
|
||||
source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam)
|
||||
data=ROOT / "data/coco128.yaml", # dataset.yaml path
|
||||
imgsz=(224, 224), # inference size (height, width)
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
view_img=False, # show results
|
||||
save_txt=False, # save results to *.txt
|
||||
nosave=False, # do not save images/videos
|
||||
augment=False, # augmented inference
|
||||
visualize=False, # visualize features
|
||||
update=False, # update all models
|
||||
project=ROOT / "runs/predict-cls", # save results to project/name
|
||||
name="exp", # save results to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
half=False, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
vid_stride=1, # video frame-rate stride
|
||||
):
|
||||
"""Conducts YOLOv5 classification inference on diverse input sources and saves results."""
|
||||
source = str(source)
|
||||
save_img = not nosave and not source.endswith(".txt") # save inference images
|
||||
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||
is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
|
||||
webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
|
||||
screenshot = source.lower().startswith("screen")
|
||||
if is_url and is_file:
|
||||
source = check_file(source) # download
|
||||
|
||||
# Directories
|
||||
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||||
(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
device = select_device(device)
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
|
||||
# Dataloader
|
||||
bs = 1 # batch_size
|
||||
if webcam:
|
||||
view_img = check_imshow(warn=True)
|
||||
dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
|
||||
bs = len(dataset)
|
||||
elif screenshot:
|
||||
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
|
||||
else:
|
||||
dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
|
||||
vid_path, vid_writer = [None] * bs, [None] * bs
|
||||
|
||||
# Run inference
|
||||
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
|
||||
seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
|
||||
for path, im, im0s, vid_cap, s in dataset:
|
||||
with dt[0]:
|
||||
im = torch.Tensor(im).to(model.device)
|
||||
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
|
||||
if len(im.shape) == 3:
|
||||
im = im[None] # expand for batch dim
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
results = model(im)
|
||||
|
||||
# Post-process
|
||||
with dt[2]:
|
||||
pred = F.softmax(results, dim=1) # probabilities
|
||||
|
||||
# Process predictions
|
||||
for i, prob in enumerate(pred): # per image
|
||||
seen += 1
|
||||
if webcam: # batch_size >= 1
|
||||
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
||||
s += f"{i}: "
|
||||
else:
|
||||
p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
|
||||
|
||||
p = Path(p) # to Path
|
||||
save_path = str(save_dir / p.name) # im.jpg
|
||||
txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt
|
||||
|
||||
s += "{:g}x{:g} ".format(*im.shape[2:]) # print string
|
||||
annotator = Annotator(im0, example=str(names), pil=True)
|
||||
|
||||
# Print results
|
||||
top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices
|
||||
s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
|
||||
|
||||
# Write results
|
||||
text = "\n".join(f"{prob[j]:.2f} {names[j]}" for j in top5i)
|
||||
if save_img or view_img: # Add bbox to image
|
||||
annotator.text([32, 32], text, txt_color=(255, 255, 255))
|
||||
if save_txt: # Write to file
|
||||
with open(f"{txt_path}.txt", "a") as f:
|
||||
f.write(text + "\n")
|
||||
|
||||
# Stream results
|
||||
im0 = annotator.result()
|
||||
if view_img:
|
||||
if platform.system() == "Linux" and p not in windows:
|
||||
windows.append(p)
|
||||
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
|
||||
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
|
||||
cv2.imshow(str(p), im0)
|
||||
cv2.waitKey(1) # 1 millisecond
|
||||
|
||||
# Save results (image with detections)
|
||||
if save_img:
|
||||
if dataset.mode == "image":
|
||||
cv2.imwrite(save_path, im0)
|
||||
else: # 'video' or 'stream'
|
||||
if vid_path[i] != save_path: # new video
|
||||
vid_path[i] = save_path
|
||||
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||
vid_writer[i].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
|
||||
|
||||
# Print results
|
||||
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
|
||||
LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
|
||||
if save_txt or save_img:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||||
if update:
|
||||
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parses command line arguments for YOLOv5 inference settings including model, source, device, and image size."""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model path(s)")
|
||||
parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[224], help="inference size h,w")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--view-img", action="store_true", help="show results")
|
||||
parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
|
||||
parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
|
||||
parser.add_argument("--augment", action="store_true", help="augmented inference")
|
||||
parser.add_argument("--visualize", action="store_true", help="visualize features")
|
||||
parser.add_argument("--update", action="store_true", help="update all models")
|
||||
parser.add_argument("--project", default=ROOT / "runs/predict-cls", help="save results to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save results to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
|
||||
parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes YOLOv5 model inference with options for ONNX DNN and video frame-rate stride adjustments."""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
|
|
@ -0,0 +1,382 @@
|
|||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
"""
|
||||
Train a YOLOv5 classifier model on a classification dataset.
|
||||
|
||||
Usage - Single-GPU training:
|
||||
$ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224
|
||||
|
||||
Usage - Multi-GPU DDP training:
|
||||
$ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
|
||||
|
||||
Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
|
||||
YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
|
||||
Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.hub as hub
|
||||
import torch.optim.lr_scheduler as lr_scheduler
|
||||
import torchvision
|
||||
from torch.cuda import amp
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from classify import val as validate
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import ClassificationModel, DetectionModel
|
||||
from utils.dataloaders import create_classification_dataloader
|
||||
from utils.general import (
|
||||
DATASETS_DIR,
|
||||
LOGGER,
|
||||
TQDM_BAR_FORMAT,
|
||||
WorkingDirectory,
|
||||
check_git_info,
|
||||
check_git_status,
|
||||
check_requirements,
|
||||
colorstr,
|
||||
download,
|
||||
increment_path,
|
||||
init_seeds,
|
||||
print_args,
|
||||
yaml_save,
|
||||
)
|
||||
from utils.loggers import GenericLogger
|
||||
from utils.plots import imshow_cls
|
||||
from utils.torch_utils import (
|
||||
ModelEMA,
|
||||
de_parallel,
|
||||
model_info,
|
||||
reshape_classifier_output,
|
||||
select_device,
|
||||
smart_DDP,
|
||||
smart_optimizer,
|
||||
smartCrossEntropyLoss,
|
||||
torch_distributed_zero_first,
|
||||
)
|
||||
|
||||
LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
|
||||
RANK = int(os.getenv("RANK", -1))
|
||||
WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
|
||||
GIT_INFO = check_git_info()
|
||||
|
||||
|
||||
def train(opt, device):
|
||||
"""Trains a YOLOv5 model, managing datasets, model optimization, logging, and saving checkpoints."""
|
||||
init_seeds(opt.seed + 1 + RANK, deterministic=True)
|
||||
save_dir, data, bs, epochs, nw, imgsz, pretrained = (
|
||||
opt.save_dir,
|
||||
Path(opt.data),
|
||||
opt.batch_size,
|
||||
opt.epochs,
|
||||
min(os.cpu_count() - 1, opt.workers),
|
||||
opt.imgsz,
|
||||
str(opt.pretrained).lower() == "true",
|
||||
)
|
||||
cuda = device.type != "cpu"
|
||||
|
||||
# Directories
|
||||
wdir = save_dir / "weights"
|
||||
wdir.mkdir(parents=True, exist_ok=True) # make dir
|
||||
last, best = wdir / "last.pt", wdir / "best.pt"
|
||||
|
||||
# Save run settings
|
||||
yaml_save(save_dir / "opt.yaml", vars(opt))
|
||||
|
||||
# Logger
|
||||
logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None
|
||||
|
||||
# Download Dataset
|
||||
with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
|
||||
data_dir = data if data.is_dir() else (DATASETS_DIR / data)
|
||||
if not data_dir.is_dir():
|
||||
LOGGER.info(f"\nDataset not found ⚠️, missing path {data_dir}, attempting download...")
|
||||
t = time.time()
|
||||
if str(data) == "imagenet":
|
||||
subprocess.run(["bash", str(ROOT / "data/scripts/get_imagenet.sh")], shell=True, check=True)
|
||||
else:
|
||||
url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{data}.zip"
|
||||
download(url, dir=data_dir.parent)
|
||||
s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
|
||||
LOGGER.info(s)
|
||||
|
||||
# Dataloaders
|
||||
nc = len([x for x in (data_dir / "train").glob("*") if x.is_dir()]) # number of classes
|
||||
trainloader = create_classification_dataloader(
|
||||
path=data_dir / "train",
|
||||
imgsz=imgsz,
|
||||
batch_size=bs // WORLD_SIZE,
|
||||
augment=True,
|
||||
cache=opt.cache,
|
||||
rank=LOCAL_RANK,
|
||||
workers=nw,
|
||||
)
|
||||
|
||||
test_dir = data_dir / "test" if (data_dir / "test").exists() else data_dir / "val" # data/test or data/val
|
||||
if RANK in {-1, 0}:
|
||||
testloader = create_classification_dataloader(
|
||||
path=test_dir,
|
||||
imgsz=imgsz,
|
||||
batch_size=bs // WORLD_SIZE * 2,
|
||||
augment=False,
|
||||
cache=opt.cache,
|
||||
rank=-1,
|
||||
workers=nw,
|
||||
)
|
||||
|
||||
# Model
|
||||
with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
|
||||
if Path(opt.model).is_file() or opt.model.endswith(".pt"):
|
||||
model = attempt_load(opt.model, device="cpu", fuse=False)
|
||||
elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0
|
||||
model = torchvision.models.__dict__[opt.model](weights="IMAGENET1K_V1" if pretrained else None)
|
||||
else:
|
||||
m = hub.list("ultralytics/yolov5") # + hub.list('pytorch/vision') # models
|
||||
raise ModuleNotFoundError(f"--model {opt.model} not found. Available models are: \n" + "\n".join(m))
|
||||
if isinstance(model, DetectionModel):
|
||||
LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
|
||||
model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model
|
||||
reshape_classifier_output(model, nc) # update class count
|
||||
for m in model.modules():
|
||||
if not pretrained and hasattr(m, "reset_parameters"):
|
||||
m.reset_parameters()
|
||||
if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
|
||||
m.p = opt.dropout # set dropout
|
||||
for p in model.parameters():
|
||||
p.requires_grad = True # for training
|
||||
model = model.to(device)
|
||||
|
||||
# Info
|
||||
if RANK in {-1, 0}:
|
||||
model.names = trainloader.dataset.classes # attach class names
|
||||
model.transforms = testloader.dataset.torch_transforms # attach inference transforms
|
||||
model_info(model)
|
||||
if opt.verbose:
|
||||
LOGGER.info(model)
|
||||
images, labels = next(iter(trainloader))
|
||||
file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / "train_images.jpg")
|
||||
logger.log_images(file, name="Train Examples")
|
||||
logger.log_graph(model, imgsz) # log model
|
||||
|
||||
# Optimizer
|
||||
optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay)
|
||||
|
||||
# Scheduler
|
||||
lrf = 0.01 # final lr (fraction of lr0)
|
||||
|
||||
# lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine
|
||||
def lf(x):
|
||||
"""Linear learning rate scheduler function, scaling learning rate from initial value to `lrf` over `epochs`."""
|
||||
return (1 - x / epochs) * (1 - lrf) + lrf # linear
|
||||
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
|
||||
# scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
|
||||
# final_div_factor=1 / 25 / lrf)
|
||||
|
||||
# EMA
|
||||
ema = ModelEMA(model) if RANK in {-1, 0} else None
|
||||
|
||||
# DDP mode
|
||||
if cuda and RANK != -1:
|
||||
model = smart_DDP(model)
|
||||
|
||||
# Train
|
||||
t0 = time.time()
|
||||
criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing) # loss function
|
||||
best_fitness = 0.0
|
||||
scaler = amp.GradScaler(enabled=cuda)
|
||||
val = test_dir.stem # 'val' or 'test'
|
||||
LOGGER.info(
|
||||
f'Image sizes {imgsz} train, {imgsz} test\n'
|
||||
f'Using {nw * WORLD_SIZE} dataloader workers\n'
|
||||
f"Logging results to {colorstr('bold', save_dir)}\n"
|
||||
f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n'
|
||||
f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}"
|
||||
)
|
||||
for epoch in range(epochs): # loop over the dataset multiple times
|
||||
tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness
|
||||
model.train()
|
||||
if RANK != -1:
|
||||
trainloader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(trainloader)
|
||||
if RANK in {-1, 0}:
|
||||
pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT)
|
||||
for i, (images, labels) in pbar: # progress bar
|
||||
images, labels = images.to(device, non_blocking=True), labels.to(device)
|
||||
|
||||
# Forward
|
||||
with amp.autocast(enabled=cuda): # stability issues when enabled
|
||||
loss = criterion(model(images), labels)
|
||||
|
||||
# Backward
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize
|
||||
scaler.unscale_(optimizer) # unscale gradients
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
if ema:
|
||||
ema.update(model)
|
||||
|
||||
if RANK in {-1, 0}:
|
||||
# Print
|
||||
tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses
|
||||
mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0) # (GB)
|
||||
pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + " " * 36
|
||||
|
||||
# Test
|
||||
if i == len(pbar) - 1: # last batch
|
||||
top1, top5, vloss = validate.run(
|
||||
model=ema.ema, dataloader=testloader, criterion=criterion, pbar=pbar
|
||||
) # test accuracy, loss
|
||||
fitness = top1 # define fitness as top1 accuracy
|
||||
|
||||
# Scheduler
|
||||
scheduler.step()
|
||||
|
||||
# Log metrics
|
||||
if RANK in {-1, 0}:
|
||||
# Best fitness
|
||||
if fitness > best_fitness:
|
||||
best_fitness = fitness
|
||||
|
||||
# Log
|
||||
metrics = {
|
||||
"train/loss": tloss,
|
||||
f"{val}/loss": vloss,
|
||||
"metrics/accuracy_top1": top1,
|
||||
"metrics/accuracy_top5": top5,
|
||||
"lr/0": optimizer.param_groups[0]["lr"],
|
||||
} # learning rate
|
||||
logger.log_metrics(metrics, epoch)
|
||||
|
||||
# Save model
|
||||
final_epoch = epoch + 1 == epochs
|
||||
if (not opt.nosave) or final_epoch:
|
||||
ckpt = {
|
||||
"epoch": epoch,
|
||||
"best_fitness": best_fitness,
|
||||
"model": deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(),
|
||||
"ema": None, # deepcopy(ema.ema).half(),
|
||||
"updates": ema.updates,
|
||||
"optimizer": None, # optimizer.state_dict(),
|
||||
"opt": vars(opt),
|
||||
"git": GIT_INFO, # {remote, branch, commit} if a git repo
|
||||
"date": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if best_fitness == fitness:
|
||||
torch.save(ckpt, best)
|
||||
del ckpt
|
||||
|
||||
# Train complete
|
||||
if RANK in {-1, 0} and final_epoch:
|
||||
LOGGER.info(
|
||||
f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
|
||||
f"\nResults saved to {colorstr('bold', save_dir)}"
|
||||
f'\nPredict: python classify/predict.py --weights {best} --source im.jpg'
|
||||
f'\nValidate: python classify/val.py --weights {best} --data {data_dir}'
|
||||
f'\nExport: python export.py --weights {best} --include onnx'
|
||||
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
|
||||
f'\nVisualize: https://netron.app\n'
|
||||
)
|
||||
|
||||
# Plot examples
|
||||
images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels
|
||||
pred = torch.max(ema.ema(images.to(device)), 1)[1]
|
||||
file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / "test_images.jpg")
|
||||
|
||||
# Log results
|
||||
meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
|
||||
logger.log_images(file, name="Test Examples (true-predicted)", epoch=epoch)
|
||||
logger.log_model(best, epochs, metadata=meta)
|
||||
|
||||
|
||||
def parse_opt(known=False):
|
||||
"""Parses command line arguments for YOLOv5 training including model path, dataset, epochs, and more, returning
|
||||
parsed arguments.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path")
|
||||
parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...")
|
||||
parser.add_argument("--epochs", type=int, default=10, help="total training epochs")
|
||||
parser.add_argument("--batch-size", type=int, default=64, help="total batch size for all GPUs")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="train, val image size (pixels)")
|
||||
parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
|
||||
parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"')
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
|
||||
parser.add_argument("--project", default=ROOT / "runs/train-cls", help="save to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--pretrained", nargs="?", const=True, default=True, help="start from i.e. --pretrained False")
|
||||
parser.add_argument("--optimizer", choices=["SGD", "Adam", "AdamW", "RMSProp"], default="Adam", help="optimizer")
|
||||
parser.add_argument("--lr0", type=float, default=0.001, help="initial learning rate")
|
||||
parser.add_argument("--decay", type=float, default=5e-5, help="weight decay")
|
||||
parser.add_argument("--label-smoothing", type=float, default=0.1, help="Label smoothing epsilon")
|
||||
parser.add_argument("--cutoff", type=int, default=None, help="Model layer cutoff index for Classify() head")
|
||||
parser.add_argument("--dropout", type=float, default=None, help="Dropout (fraction)")
|
||||
parser.add_argument("--verbose", action="store_true", help="Verbose mode")
|
||||
parser.add_argument("--seed", type=int, default=0, help="Global training seed")
|
||||
parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
|
||||
return parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes YOLOv5 training with given options, handling device setup and DDP mode; includes pre-training checks."""
|
||||
if RANK in {-1, 0}:
|
||||
print_args(vars(opt))
|
||||
check_git_status()
|
||||
check_requirements(ROOT / "requirements.txt")
|
||||
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if LOCAL_RANK != -1:
|
||||
assert opt.batch_size != -1, "AutoBatch is coming soon for classification, please pass a valid --batch-size"
|
||||
assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE"
|
||||
assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command"
|
||||
torch.cuda.set_device(LOCAL_RANK)
|
||||
device = torch.device("cuda", LOCAL_RANK)
|
||||
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
|
||||
|
||||
# Parameters
|
||||
opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
|
||||
|
||||
# Train
|
||||
train(opt, device)
|
||||
|
||||
|
||||
def run(**kwargs):
|
||||
"""
|
||||
Executes YOLOv5 model training or inference with specified parameters, returning updated options.
|
||||
|
||||
Example: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
|
||||
"""
|
||||
opt = parse_opt(True)
|
||||
for k, v in kwargs.items():
|
||||
setattr(opt, k, v)
|
||||
main(opt)
|
||||
return opt
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,178 @@
|
|||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
"""
|
||||
Validate a trained YOLOv5 classification model on a classification dataset.
|
||||
|
||||
Usage:
|
||||
$ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
|
||||
$ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet
|
||||
|
||||
Usage - formats:
|
||||
$ python classify/val.py --weights yolov5s-cls.pt # PyTorch
|
||||
yolov5s-cls.torchscript # TorchScript
|
||||
yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s-cls_openvino_model # OpenVINO
|
||||
yolov5s-cls.engine # TensorRT
|
||||
yolov5s-cls.mlmodel # CoreML (macOS-only)
|
||||
yolov5s-cls_saved_model # TensorFlow SavedModel
|
||||
yolov5s-cls.pb # TensorFlow GraphDef
|
||||
yolov5s-cls.tflite # TensorFlow Lite
|
||||
yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s-cls_paddle_model # PaddlePaddle
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.dataloaders import create_classification_dataloader
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
TQDM_BAR_FORMAT,
|
||||
Profile,
|
||||
check_img_size,
|
||||
check_requirements,
|
||||
colorstr,
|
||||
increment_path,
|
||||
print_args,
|
||||
)
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
data=ROOT / "../datasets/mnist", # dataset dir
|
||||
weights=ROOT / "yolov5s-cls.pt", # model.pt path(s)
|
||||
batch_size=128, # batch size
|
||||
imgsz=224, # inference size (pixels)
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
workers=8, # max dataloader workers (per RANK in DDP mode)
|
||||
verbose=False, # verbose output
|
||||
project=ROOT / "runs/val-cls", # save to project/name
|
||||
name="exp", # save to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
half=False, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
model=None,
|
||||
dataloader=None,
|
||||
criterion=None,
|
||||
pbar=None,
|
||||
):
|
||||
"""Validates a YOLOv5 classification model on a dataset, computing metrics like top1 and top5 accuracy."""
|
||||
# Initialize/load model and set device
|
||||
training = model is not None
|
||||
if training: # called by train.py
|
||||
device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
|
||||
half &= device.type != "cpu" # half precision only supported on CUDA
|
||||
model.half() if half else model.float()
|
||||
else: # called directly
|
||||
device = select_device(device, batch_size=batch_size)
|
||||
|
||||
# Directories
|
||||
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||||
save_dir.mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
|
||||
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
half = model.fp16 # FP16 supported on limited backends with CUDA
|
||||
if engine:
|
||||
batch_size = model.batch_size
|
||||
else:
|
||||
device = model.device
|
||||
if not (pt or jit):
|
||||
batch_size = 1 # export.py models default to batch-size 1
|
||||
LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
|
||||
|
||||
# Dataloader
|
||||
data = Path(data)
|
||||
test_dir = data / "test" if (data / "test").exists() else data / "val" # data/test or data/val
|
||||
dataloader = create_classification_dataloader(
|
||||
path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers
|
||||
)
|
||||
|
||||
model.eval()
|
||||
pred, targets, loss, dt = [], [], 0, (Profile(device=device), Profile(device=device), Profile(device=device))
|
||||
n = len(dataloader) # number of batches
|
||||
action = "validating" if dataloader.dataset.root.stem == "val" else "testing"
|
||||
desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
|
||||
bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
|
||||
with torch.cuda.amp.autocast(enabled=device.type != "cpu"):
|
||||
for images, labels in bar:
|
||||
with dt[0]:
|
||||
images, labels = images.to(device, non_blocking=True), labels.to(device)
|
||||
|
||||
with dt[1]:
|
||||
y = model(images)
|
||||
|
||||
with dt[2]:
|
||||
pred.append(y.argsort(1, descending=True)[:, :5])
|
||||
targets.append(labels)
|
||||
if criterion:
|
||||
loss += criterion(y, labels)
|
||||
|
||||
loss /= n
|
||||
pred, targets = torch.cat(pred), torch.cat(targets)
|
||||
correct = (targets[:, None] == pred).float()
|
||||
acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy
|
||||
top1, top5 = acc.mean(0).tolist()
|
||||
|
||||
if pbar:
|
||||
pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
|
||||
if verbose: # all classes
|
||||
LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
|
||||
LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
|
||||
for i, c in model.names.items():
|
||||
acc_i = acc[targets == i]
|
||||
top1i, top5i = acc_i.mean(0).tolist()
|
||||
LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
|
||||
|
||||
# Print results
|
||||
t = tuple(x.t / len(dataloader.dataset.samples) * 1e3 for x in dt) # speeds per image
|
||||
shape = (1, 3, imgsz, imgsz)
|
||||
LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}" % t)
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
|
||||
|
||||
return top1, top5, loss
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parses and returns command line arguments for YOLOv5 model evaluation and inference settings."""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data", type=str, default=ROOT / "../datasets/mnist", help="dataset path")
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model.pt path(s)")
|
||||
parser.add_argument("--batch-size", type=int, default=128, help="batch size")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="inference size (pixels)")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
|
||||
parser.add_argument("--verbose", nargs="?", const=True, default=True, help="verbose output")
|
||||
parser.add_argument("--project", default=ROOT / "runs/val-cls", help="save to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
|
||||
opt = parser.parse_args()
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes the YOLOv5 model prediction workflow, handling argument parsing and requirement checks."""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
|
|
@ -0,0 +1,72 @@
|
|||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
|
||||
# Example usage: python train.py --data Argoverse.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── Argoverse ← downloads here (31.3 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/Argoverse # dataset root dir
|
||||
train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
|
||||
val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
|
||||
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: bus
|
||||
5: truck
|
||||
6: traffic_light
|
||||
7: stop_sign
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import json
|
||||
|
||||
from tqdm import tqdm
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
def argoverse2yolo(set):
|
||||
labels = {}
|
||||
a = json.load(open(set, "rb"))
|
||||
for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
|
||||
img_id = annot['image_id']
|
||||
img_name = a['images'][img_id]['name']
|
||||
img_label_name = f'{img_name[:-3]}txt'
|
||||
|
||||
cls = annot['category_id'] # instance class id
|
||||
x_center, y_center, width, height = annot['bbox']
|
||||
x_center = (x_center + width / 2) / 1920.0 # offset and scale
|
||||
y_center = (y_center + height / 2) / 1200.0 # offset and scale
|
||||
width /= 1920.0 # scale
|
||||
height /= 1200.0 # scale
|
||||
|
||||
img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
|
||||
if not img_dir.exists():
|
||||
img_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
k = str(img_dir / img_label_name)
|
||||
if k not in labels:
|
||||
labels[k] = []
|
||||
labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
|
||||
|
||||
for k in labels:
|
||||
with open(k, "w") as f:
|
||||
f.writelines(labels[k])
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
|
||||
download(urls, dir=dir, delete=False)
|
||||
|
||||
# Convert
|
||||
annotations_dir = 'Argoverse-HD/annotations/'
|
||||
(dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
|
||||
for d in "train.json", "val.json":
|
||||
argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
|
||||
# Example usage: python train.py --data GlobalWheat2020.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── GlobalWheat2020 ← downloads here (7.0 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/GlobalWheat2020 # dataset root dir
|
||||
train: # train images (relative to 'path') 3422 images
|
||||
- images/arvalis_1
|
||||
- images/arvalis_2
|
||||
- images/arvalis_3
|
||||
- images/ethz_1
|
||||
- images/rres_1
|
||||
- images/inrae_1
|
||||
- images/usask_1
|
||||
val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
|
||||
- images/ethz_1
|
||||
test: # test images (optional) 1276 images
|
||||
- images/utokyo_1
|
||||
- images/utokyo_2
|
||||
- images/nau_1
|
||||
- images/uq_1
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: wheat_head
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
|
||||
'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip']
|
||||
download(urls, dir=dir)
|
||||
|
||||
# Make Directories
|
||||
for p in 'annotations', 'images', 'labels':
|
||||
(dir / p).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Move
|
||||
for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
|
||||
'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
|
||||
(dir / p).rename(dir / 'images' / p) # move to /images
|
||||
f = (dir / p).with_suffix('.json') # json file
|
||||
if f.exists():
|
||||
f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
|
||||
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