Creado con Colaboratory
This commit is contained in:
Glenn Jocher
parent
394131c2aa
commit
46c43b7b17
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"source": [
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"!git clone https://github.com/ultralytics/yolov5 # clone repo\n",
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@ -563,7 +563,7 @@
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"clear_output()\n",
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"print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
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],
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"outputId": "c9a308f7-2216-4805-8003-eca8dd0dc30d"
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"source": [
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"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",
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"source": [
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"# Download COCO val2017\n",
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"torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n",
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"!unzip -q tmp.zip -d ../ && rm tmp.zip"
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],
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"execution_count": 2,
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"outputId": "013935a5-ba81-4810-b723-0cb01cf7bc79"
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"source": [
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"# Run YOLOv5x on COCO val2017\n",
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"!python test.py --weights yolov5x.pt --data coco.yaml --img 640"
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],
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"execution_count": null,
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"execution_count": 3,
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"text": [
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"Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', img_size=640, iou_thres=0.65, save_conf=False, save_dir='runs/test', save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
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"Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
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"Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
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"\n",
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"Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5x.pt to yolov5x.pt...\n",
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"100% 170M/170M [00:05<00:00, 32.2MB/s]\n",
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"100% 170M/170M [00:05<00:00, 32.6MB/s]\n",
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"\n",
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"Fusing layers... \n",
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"Model Summary: 284 layers, 8.89222e+07 parameters, 0 gradients\n",
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"Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 16239.02it/s]\n",
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" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:22<00:00, 1.89it/s]\n",
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" all 5e+03 3.63e+04 0.409 0.754 0.672 0.483\n",
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"Speed: 5.9/2.0/7.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
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"Model Summary: 484 layers, 88922205 parameters, 0 gradients\n",
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"Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 14785.71it/s]\n",
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" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:30<00:00, 1.74it/s]\n",
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" all 5e+03 3.63e+04 0.409 0.754 0.672 0.484\n",
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"Speed: 5.9/2.1/7.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
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"\n",
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"COCO mAP with pycocotools... saving runs/test/detections_val2017_yolov5x_results.json...\n",
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"Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n",
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"loading annotations into memory...\n",
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"Done (t=0.73s)\n",
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"Done (t=0.43s)\n",
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"creating index...\n",
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"index created!\n",
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"Loading and preparing results...\n",
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"DONE (t=4.48s)\n",
|
||||
"DONE (t=4.67s)\n",
|
||||
"creating index...\n",
|
||||
"index created!\n",
|
||||
"Running per image evaluation...\n",
|
||||
"Evaluate annotation type *bbox*\n",
|
||||
"DONE (t=86.55s).\n",
|
||||
"DONE (t=92.11s).\n",
|
||||
"Accumulating evaluation results...\n",
|
||||
"DONE (t=13.15s).\n",
|
||||
"DONE (t=13.24s).\n",
|
||||
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.492\n",
|
||||
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.676\n",
|
||||
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.534\n",
|
||||
|
|
@ -773,7 +773,7 @@
|
|||
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.493\n",
|
||||
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.723\n",
|
||||
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812\n",
|
||||
"Results saved to runs/test\n"
|
||||
"Results saved to runs/test/exp\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
|
|
@ -831,34 +831,34 @@
|
|||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "Knxi2ncxWffW",
|
||||
"outputId": "8c237907-6c62-4273-ce27-d5035fc6f5ac",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 66,
|
||||
"referenced_widgets": [
|
||||
"b434b178be4b41b3881e237e19f49b45",
|
||||
"72db749d8e3840238e1ceeec58a2cb4c",
|
||||
"4459ef1aa32e422c9f1bc152a2aba7dc",
|
||||
"b658312802194c1d86383e444af6ade4",
|
||||
"e24e99052e1a4f9ea794081fc6c42d80",
|
||||
"a08dda95df7441739105f5b59b8ea882",
|
||||
"1abb5618e7134f0eb976857e126fda0d",
|
||||
"67e56da5b8574fc7a715422bdfeaeab4"
|
||||
"cf1ab9fde7444d3e874fcd407ba8f0f8",
|
||||
"9ee03f9c85f34155b2645e89c9211547",
|
||||
"933ebc451c09490aadf71afbbb3dff2a",
|
||||
"8e7c55cbca624432a84fa7ad8f3a4016",
|
||||
"dd62d83b35d04a178840772e82bd2f2e",
|
||||
"d5c4f3d1c8b046e3a163faaa6b3a51ab",
|
||||
"78d1da8efb504b03878ca9ce5b404006",
|
||||
"d28208ba1213436a93926a01d99d97ae"
|
||||
]
|
||||
}
|
||||
},
|
||||
"outputId": "59f9a94b-21e1-4626-f36a-a8e1b1e5c8f6"
|
||||
},
|
||||
"source": [
|
||||
"# Download COCO128\n",
|
||||
"torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n",
|
||||
"!unzip -q tmp.zip -d ../ && rm tmp.zip"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 4,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "display_data",
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "b434b178be4b41b3881e237e19f49b45",
|
||||
"model_id": "cf1ab9fde7444d3e874fcd407ba8f0f8",
|
||||
"version_minor": 0,
|
||||
"version_major": 2
|
||||
},
|
||||
|
|
@ -907,27 +907,29 @@
|
|||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "1NcFxRcFdJ_O",
|
||||
"outputId": "a98e611d-979b-4e8c-d61c-8e219958ed33",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
}
|
||||
},
|
||||
"outputId": "138f2d1d-364c-405a-cf13-ea91a2aff915"
|
||||
},
|
||||
"source": [
|
||||
"# Train YOLOv5s on COCO128 for 3 epochs\n",
|
||||
"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --nosave --cache"
|
||||
],
|
||||
"execution_count": null,
|
||||
"execution_count": 5,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
|
||||
"\n",
|
||||
"Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=10, logdir='runs/', multi_scale=False, name='', noautoanchor=False, nosave=True, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
|
||||
"Start Tensorboard with \"tensorboard --logdir runs/\", view at http://localhost:6006/\n",
|
||||
"2020-11-05 16:39:40.555423: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
|
||||
"Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)\n",
|
||||
"Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
|
||||
"Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n",
|
||||
"2020-11-20 11:45:17.042357: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
|
||||
"Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0}\n",
|
||||
"Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt...\n",
|
||||
"100% 14.5M/14.5M [00:01<00:00, 14.8MB/s]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" from n params module arguments \n",
|
||||
" 0 -1 1 3520 models.common.Focus [3, 32, 3] \n",
|
||||
|
|
@ -955,15 +957,15 @@
|
|||
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
|
||||
" 23 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n",
|
||||
" 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
|
||||
"Model Summary: 191 layers, 7.46816e+06 parameters, 7.46816e+06 gradients\n",
|
||||
"Model Summary: 283 layers, 7468157 parameters, 7468157 gradients\n",
|
||||
"\n",
|
||||
"Transferred 370/370 items from yolov5s.pt\n",
|
||||
"Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n",
|
||||
"Scanning images: 100% 128/128 [00:00<00:00, 5375.86it/s]\n",
|
||||
"Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 15114.61it/s]\n",
|
||||
"Caching images (0.1GB): 100% 128/128 [00:00<00:00, 141.99it/s]\n",
|
||||
"Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 12572.50it/s]\n",
|
||||
"Caching images (0.1GB): 100% 128/128 [00:01<00:00, 79.68it/s] \n",
|
||||
"Scanning images: 100% 128/128 [00:00<00:00, 5395.63it/s]\n",
|
||||
"Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 13972.28it/s]\n",
|
||||
"Caching images (0.1GB): 100% 128/128 [00:00<00:00, 173.55it/s]\n",
|
||||
"Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 8693.98it/s]\n",
|
||||
"Caching images (0.1GB): 100% 128/128 [00:00<00:00, 133.30it/s]\n",
|
||||
"NumExpr defaulting to 2 threads.\n",
|
||||
"\n",
|
||||
"Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
|
||||
|
|
@ -973,22 +975,21 @@
|
|||
"Starting training for 3 epochs...\n",
|
||||
"\n",
|
||||
" Epoch gpu_mem box obj cls total targets img_size\n",
|
||||
" 0/2 3.22G 0.04202 0.06746 0.01503 0.1245 194 640: 100% 8/8 [00:03<00:00, 2.51it/s]\n",
|
||||
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:03<00:00, 2.42it/s]\n",
|
||||
" all 128 929 0.405 0.762 0.701 0.449\n",
|
||||
" 0/2 5.24G 0.04202 0.06745 0.01503 0.1245 194 640: 100% 8/8 [00:03<00:00, 2.01it/s]\n",
|
||||
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:03<00:00, 2.40it/s]\n",
|
||||
" all 128 929 0.404 0.758 0.701 0.45\n",
|
||||
"\n",
|
||||
" Epoch gpu_mem box obj cls total targets img_size\n",
|
||||
" 1/2 3.17G 0.04461 0.05873 0.01689 0.1202 142 640: 100% 8/8 [00:01<00:00, 4.14it/s]\n",
|
||||
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:01<00:00, 6.49it/s]\n",
|
||||
" all 128 929 0.402 0.772 0.703 0.452\n",
|
||||
" 1/2 5.12G 0.04461 0.05874 0.0169 0.1202 142 640: 100% 8/8 [00:01<00:00, 4.14it/s]\n",
|
||||
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:01<00:00, 5.75it/s]\n",
|
||||
" all 128 929 0.403 0.772 0.703 0.453\n",
|
||||
"\n",
|
||||
" Epoch gpu_mem box obj cls total targets img_size\n",
|
||||
" 2/2 3.17G 0.04445 0.06545 0.01666 0.1266 149 640: 100% 8/8 [00:01<00:00, 4.33it/s]\n",
|
||||
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 2.78it/s]\n",
|
||||
" all 128 929 0.395 0.766 0.701 0.455\n",
|
||||
" 2/2 5.12G 0.04445 0.06545 0.01667 0.1266 149 640: 100% 8/8 [00:01<00:00, 4.15it/s]\n",
|
||||
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:06<00:00, 1.18it/s]\n",
|
||||
" all 128 929 0.395 0.767 0.702 0.452\n",
|
||||
"Optimizer stripped from runs/train/exp/weights/last.pt, 15.2MB\n",
|
||||
"Optimizer stripped from runs/train/exp/weights/best.pt, 15.2MB\n",
|
||||
"3 epochs completed in 0.005 hours.\n",
|
||||
"3 epochs completed in 0.006 hours.\n",
|
||||
"\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
|
|
@ -1140,10 +1141,9 @@
|
|||
"id": "mcKoSIK2WSzj"
|
||||
},
|
||||
"source": [
|
||||
"# Test all models\n",
|
||||
"# Test all\n",
|
||||
"%%shell\n",
|
||||
"for x in s m l x\n",
|
||||
"do\n",
|
||||
"for x in s m l x; do\n",
|
||||
" python test.py --weights yolov5$x.pt --data coco.yaml --img 640\n",
|
||||
"done"
|
||||
],
|
||||
|
|
@ -1156,26 +1156,22 @@
|
|||
"id": "FGH0ZjkGjejy"
|
||||
},
|
||||
"source": [
|
||||
"# Run unit tests\n",
|
||||
"# Unit tests\n",
|
||||
"%%shell\n",
|
||||
"# git clone https://github.com/ultralytics/yolov5\n",
|
||||
"# cd yolov5\n",
|
||||
"pip install -qr requirements.txt onnx\n",
|
||||
"export PYTHONPATH=\"$PWD\" # to run *.py. files in subdirectories\n",
|
||||
"\n",
|
||||
"export PYTHONPATH=\"$PWD\" # to run *.py. files in subdirectories\n",
|
||||
"for x in yolov5s #yolov5m yolov5l yolov5x # models\n",
|
||||
"do\n",
|
||||
" rm -rf runs\n",
|
||||
" python train.py --weights $x.pt --cfg $x.yaml --epochs 3 --img 320 --device 0 # train\n",
|
||||
" for di in 0 cpu # inference devices\n",
|
||||
" do\n",
|
||||
" python detect.py --weights $x.pt --device $di # detect official\n",
|
||||
" python detect.py --weights runs/train/exp/weights/last.pt --device $di # detect custom\n",
|
||||
" python test.py --weights $x.pt --device $di # test official\n",
|
||||
" python test.py --weights runs/train/exp/weights/last.pt --device $di # test custom\n",
|
||||
"for m in yolov5s; do # models\n",
|
||||
" python train.py --weights $m.pt --epochs 3 --img 320 --device 0 # train pretrained\n",
|
||||
" python train.py --cfg $m.yaml --epochs 3 --img 320 --device 0 # train scratch\n",
|
||||
" for d in 0 cpu; do # devices\n",
|
||||
" python detect.py --weights $m.pt --device $d # detect official\n",
|
||||
" python detect.py --weights runs/train/exp/weights/best.pt --device $d # detect custom\n",
|
||||
" python test.py --weights $m.pt --device $d # test official\n",
|
||||
" python test.py --weights runs/train/exp/weights/best.pt --device $d # test custom\n",
|
||||
" done\n",
|
||||
" python models/yolo.py --cfg $x.yaml # inspect\n",
|
||||
" python models/export.py --weights $x.pt --img 640 --batch 1 # export\n",
|
||||
" python hubconf.py # hub\n",
|
||||
" python models/yolo.py --cfg $m.yaml # inspect\n",
|
||||
" python models/export.py --weights $m.pt --img 640 --batch 1 # export\n",
|
||||
"done"
|
||||
],
|
||||
"execution_count": null,
|
||||
|
|
|
|||
Loading…
Reference in New Issue