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classify/tutorial.ipynb
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......@@ -42,14 +42,14 @@
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"YOLOv5 🚀 v6.2-258-g7fc7ed7 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
"YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
]
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"\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['yolov5s-cls.pt'], source=data/images, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=True, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\n",
"YOLOv5 🚀 v6.2-258-g7fc7ed7 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['yolov5s-cls.pt'], source=data/images, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\n",
"YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt to yolov5s-cls.pt...\n",
"100% 10.5M/10.5M [00:03<00:00, 2.94MB/s]\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to yolov5s-cls.pt...\n",
"100% 10.5M/10.5M [00:00<00:00, 12.3MB/s]\n",
"\n",
"Fusing layers... \n",
"Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 224x224 minibus 0.39, police van 0.24, amphibious vehicle 0.05, recreational vehicle 0.04, trolleybus 0.03, 3.9ms\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 224x224 suit 0.38, bow tie 0.19, bridegroom 0.18, rugby ball 0.04, stage 0.02, 4.1ms\n",
"Speed: 0.3ms pre-process, 4.0ms inference, 1.5ms NMS per image at shape (1, 3, 224, 224)\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 224x224 suit 0.38, bow tie 0.19, bridegroom 0.18, rugby ball 0.04, stage 0.02, 4.6ms\n",
"Speed: 0.3ms pre-process, 4.3ms inference, 1.5ms NMS per image at shape (1, 3, 224, 224)\n",
"Results saved to \u001b[1mruns/predict-cls/exp\u001b[0m\n"
]
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......@@ -155,23 +155,23 @@
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"text": [
"--2022-11-18 21:48:38-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n",
"--2022-11-22 19:53:40-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n",
"Resolving image-net.org (image-net.org)... 171.64.68.16\n",
"Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 6744924160 (6.3G) [application/x-tar]\n",
"Saving to: ‘ILSVRC2012_img_val.tar’\n",
"\n",
"ILSVRC2012_img_val. 100%[===================>] 6.28G 7.15MB/s in 11m 13s \n",
"ILSVRC2012_img_val. 100%[===================>] 6.28G 16.1MB/s in 10m 52s \n",
"\n",
"2022-11-18 21:59:52 (9.55 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\n",
"2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\n",
"\n"
]
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......@@ -189,7 +189,7 @@
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......@@ -197,11 +197,11 @@
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"\u001b[34m\u001b[1mclassify/val: \u001b[0mdata=../datasets/imagenet, weights=['yolov5s-cls.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=True, dnn=False\n",
"YOLOv5 🚀 v6.2-258-g7fc7ed7 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
"Fusing layers... \n",
"Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n",
"validating: 100% 391/391 [04:48<00:00, 1.35it/s]\n",
"validating: 100% 391/391 [04:57<00:00, 1.31it/s]\n",
" Class Images top1_acc top5_acc\n",
" all 50000 0.715 0.902\n",
" tench 50 0.94 0.98\n",
......@@ -1269,30 +1269,30 @@
},
{
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"\u001b[34m\u001b[1mclassify/train: \u001b[0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=3, batch_size=16, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n",
"\u001b[34m\u001b[1mclassify/train: \u001b[0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=5, batch_size=64, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v6.2-258-g7fc7ed7 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n",
"\n",
"Dataset not found ⚠️, missing path /content/datasets/imagenette160, attempting download...\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenette160.zip to /content/datasets/imagenette160.zip...\n",
"100% 103M/103M [00:09<00:00, 11.1MB/s]\n",
"100% 103M/103M [00:00<00:00, 347MB/s] \n",
"Unzipping /content/datasets/imagenette160.zip...\n",
"Dataset download success ✅ (13.2s), saved to \u001b[1m/content/datasets/imagenette160\u001b[0m\n",
"Dataset download success ✅ (3.3s), saved to \u001b[1m/content/datasets/imagenette160\u001b[0m\n",
"\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n",
"Model summary: 149 layers, 4185290 parameters, 4185290 gradients, 10.5 GFLOPs\n",
......@@ -1300,14 +1300,16 @@
"Image sizes 224 train, 224 test\n",
"Using 1 dataloader workers\n",
"Logging results to \u001b[1mruns/train-cls/exp\u001b[0m\n",
"Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 3 epochs...\n",
"Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 5 epochs...\n",
"\n",
" Epoch GPU_mem train_loss val_loss top1_acc top5_acc\n",
" 1/3 0.348G 1.31 1.09 0.794 0.979: 100% 592/592 [01:02<00:00, 9.47it/s]\n",
" 2/3 0.415G 1.09 0.852 0.883 0.99: 100% 592/592 [00:59<00:00, 10.00it/s]\n",
" 3/3 0.415G 0.954 0.776 0.907 0.994: 100% 592/592 [00:59<00:00, 9.89it/s]\n",
" 1/5 1.47G 1.05 0.974 0.828 0.975: 100% 148/148 [00:38<00:00, 3.82it/s]\n",
" 2/5 1.73G 0.895 0.766 0.911 0.994: 100% 148/148 [00:36<00:00, 4.03it/s]\n",
" 3/5 1.73G 0.82 0.704 0.934 0.996: 100% 148/148 [00:35<00:00, 4.20it/s]\n",
" 4/5 1.73G 0.766 0.664 0.951 0.998: 100% 148/148 [00:36<00:00, 4.05it/s]\n",
" 5/5 1.73G 0.724 0.634 0.959 0.997: 100% 148/148 [00:37<00:00, 3.94it/s]\n",
"\n",
"Training complete (0.051 hours)\n",
"Training complete (0.052 hours)\n",
"Results saved to \u001b[1mruns/train-cls/exp\u001b[0m\n",
"Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg\n",
"Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data /content/datasets/imagenette160\n",
......@@ -1320,7 +1322,7 @@
],
"source": [
"# Train YOLOv5s Classification on Imagenette160 for 3 epochs\n",
"!python classify/train.py --img 224 --batch 16 --epochs 3 --data imagenette160 --model yolov5s-cls.pt --cache"
"!python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 --cache"
]
},
{
......@@ -1452,8 +1454,7 @@
"accelerator": "GPU",
"colab": {
"name": "YOLOv5 Classification Tutorial",
"provenance": [],
"toc_visible": true
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
......
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