Merge remote-tracking branch 'origin/master'
This commit is contained in:
Glenn Jocher
commit
ea34f848a6
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@ -21,7 +21,7 @@ This repository represents Ultralytics open-source research into future object d
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| [YOLOv5m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 43.4 | 43.4 | 62.4 | 3.0ms | 333 || 21.8M | 39.4B
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| [YOLOv5l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 46.6 | 46.7 | 65.4 | 3.9ms | 256 || 47.8M | 88.1B
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| [YOLOv5x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | **48.4** | **48.4** | **66.9** | 6.1ms | 164 || 89.0M | 166.4B
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| [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B
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| [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B
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** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.
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@ -54,10 +54,11 @@ $ pip install -U -r requirements.txt
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Inference can be run on most common media formats. Model [checkpoints](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) are downloaded automatically if available. Results are saved to `./inference/output`.
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```bash
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$ python detect.py --source file.jpg # image
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$ python detect.py --source 0 # webcam
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file.jpg # image
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file.mp4 # video
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./dir # directory
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0 # webcam
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path/ # directory
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path/*.jpg # glob
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rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
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http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
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```
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@ -1,13 +1,13 @@
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# COCO 2017 dataset http://cocodataset.org
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# Download command: bash yolov5/data/get_coco2017.sh
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# Train command: python train.py --data ./data/coco.yaml
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# Dataset should be placed next to yolov5 folder:
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# Train command: python train.py --data coco.yaml
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# Default dataset location is next to /yolov5:
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# /parent_folder
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# /coco
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# /yolov5
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# train and val datasets (image directory or *.txt file with image paths)
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../coco/train2017.txt # 118k images
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val: ../coco/val2017.txt # 5k images
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test: ../coco/test-dev2017.txt # 20k images for submission to https://competitions.codalab.org/competitions/20794
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@ -1,15 +1,15 @@
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# COCO 2017 dataset http://cocodataset.org - first 128 training images
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# Download command: python -c "from yolov5.utils.google_utils import gdrive_download; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip')"
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# Train command: python train.py --data ./data/coco128.yaml
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# Dataset should be placed next to yolov5 folder:
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# Download command: python -c "from yolov5.utils.google_utils import *; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', 'coco128.zip')"
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# Train command: python train.py --data coco128.yaml
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# Default dataset location is next to /yolov5:
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# /parent_folder
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# /coco128
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# /yolov5
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# train and val datasets (image directory or *.txt file with image paths)
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train: ../coco128/images/train2017/
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val: ../coco128/images/train2017/
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../coco128/images/train2017/ # 128 images
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val: ../coco128/images/train2017/ # 128 images
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# number of classes
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nc: 80
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@ -1,12 +1,13 @@
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#!/bin/bash
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# COCO 2017 dataset http://cocodataset.org
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# Download command: bash yolov5/data/get_coco2017.sh
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# Train command: python train.py --data ./data/coco.yaml
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# Dataset should be placed next to yolov5 folder:
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# Train command: python train.py --data coco.yaml
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# Default dataset location is next to /yolov5:
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# /parent_folder
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# /coco
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# /yolov5
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# Download labels from Google Drive, accepting presented query
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filename="coco2017labels.zip"
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fileid="1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L"
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@ -1,11 +1,12 @@
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# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
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# Download command: bash ./data/get_voc.sh
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# Train command: python train.py --data voc.yaml
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# Dataset should be placed next to yolov5 folder:
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# Default dataset location is next to /yolov5:
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# /parent_folder
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# /VOC
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# /yolov5
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start=`date +%s`
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# handle optional download dir
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@ -1,14 +1,15 @@
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# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
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# Download command: bash ./data/get_voc.sh
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# Train command: python train.py --data voc.yaml
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# Dataset should be placed next to yolov5 folder:
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# Default dataset location is next to /yolov5:
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# /parent_folder
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# /VOC
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# /yolov5
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# train and val datasets (image directory or *.txt file with image paths)
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train: ../VOC/images/train/
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val: ../VOC/images/val/
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../VOC/images/train/ # 16551 images
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val: ../VOC/images/val/ # 4952 images
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# number of classes
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nc: 20
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4
test.py
4
test.py
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@ -41,9 +41,9 @@ def test(data,
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# model = nn.DataParallel(model)
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# Half
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half = device.type != 'cpu' and torch.cuda.device_count() == 1 # half precision only supported on single-GPU
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half = device.type != 'cpu' # half precision only supported on CUDA
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if half:
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model.half() # to FP16
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model.half()
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# Configure
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model.eval()
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@ -68,35 +68,39 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
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class LoadImages: # for inference
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def __init__(self, path, img_size=640):
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path = str(Path(path)) # os-agnostic
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files = []
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if os.path.isdir(path):
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files = sorted(glob.glob(os.path.join(path, '*.*')))
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elif os.path.isfile(path):
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files = [path]
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p = str(Path(path)) # os-agnostic
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p = os.path.abspath(p) # absolute path
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if '*' in p:
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files = sorted(glob.glob(p)) # glob
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elif os.path.isdir(p):
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files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir
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elif os.path.isfile(p):
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files = [p] # files
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else:
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raise Exception('ERROR: %s does not exist' % p)
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images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
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videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
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nI, nV = len(images), len(videos)
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ni, nv = len(images), len(videos)
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self.img_size = img_size
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self.files = images + videos
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self.nF = nI + nV # number of files
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self.video_flag = [False] * nI + [True] * nV
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self.nf = ni + nv # number of files
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self.video_flag = [False] * ni + [True] * nv
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self.mode = 'images'
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if any(videos):
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self.new_video(videos[0]) # new video
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else:
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self.cap = None
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assert self.nF > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \
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(path, img_formats, vid_formats)
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assert self.nf > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \
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(p, img_formats, vid_formats)
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def __iter__(self):
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self.count = 0
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return self
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def __next__(self):
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if self.count == self.nF:
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if self.count == self.nf:
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raise StopIteration
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path = self.files[self.count]
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@ -107,7 +111,7 @@ class LoadImages: # for inference
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if not ret_val:
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self.count += 1
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self.cap.release()
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if self.count == self.nF: # last video
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if self.count == self.nf: # last video
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raise StopIteration
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else:
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path = self.files[self.count]
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@ -115,14 +119,14 @@ class LoadImages: # for inference
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ret_val, img0 = self.cap.read()
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self.frame += 1
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print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nF, self.frame, self.nframes, path), end='')
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print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nf, self.frame, self.nframes, path), end='')
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else:
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# Read image
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self.count += 1
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img0 = cv2.imread(path) # BGR
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assert img0 is not None, 'Image Not Found ' + path
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print('image %g/%g %s: ' % (self.count, self.nF, path), end='')
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print('image %g/%g %s: ' % (self.count, self.nf, path), end='')
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# Padded resize
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img = letterbox(img0, new_shape=self.img_size)[0]
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@ -140,7 +144,7 @@ class LoadImages: # for inference
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self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
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def __len__(self):
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return self.nF # number of files
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return self.nf # number of files
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class LoadWebcam: # for inference
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@ -470,6 +474,13 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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img, labels = load_mosaic(self, index)
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shapes = None
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# MixUp https://arxiv.org/pdf/1710.09412.pdf
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# if random.random() < 0.5:
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# img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
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# r = np.random.beta(0.3, 0.3) # mixup ratio, alpha=beta=0.3
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# img = (img * r + img2 * (1 - r)).astype(np.uint8)
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# labels = np.concatenate((labels, labels2), 0)
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else:
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# Load image
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img, (h0, w0), (h, w) = load_image(self, index)
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@ -195,6 +195,8 @@ class ModelEMA:
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def __init__(self, model, decay=0.9999, updates=0):
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# Create EMA
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self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA
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if next(model.parameters()).device.type != 'cpu':
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self.ema.half() # FP16 EMA
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self.updates = updates # number of EMA updates
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self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs)
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for p in self.ema.parameters():
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