Created using Colaboratory
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@ -415,7 +415,7 @@
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"import utils\n",
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"display = utils.notebook_init() # checks"
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],
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"execution_count": 1,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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@ -467,7 +467,7 @@
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"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
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"# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
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],
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"execution_count": 2,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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@ -547,7 +547,7 @@
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"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n",
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"!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
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],
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"execution_count": 3,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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@ -578,7 +578,7 @@
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"# Run YOLOv5x on COCO val\n",
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"!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
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],
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"execution_count": 4,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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@ -751,7 +751,7 @@
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"# Train YOLOv5s on COCO128 for 3 epochs\n",
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"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
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],
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"execution_count": 5,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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@ -1113,7 +1113,7 @@
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"cell_type": "code",
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"source": [
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"# Classification\n",
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"for m in [*(f'yolov5{x}.pt' for x in 'nsmlx'), 'resnet50.pt', 'efficientnet_b0.pt']:\n",
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"for m in [*(f'yolov5{x}-cls.pt' for x in 'nsmlx'), 'resnet50.pt', 'efficientnet_b0.pt']:\n",
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" for d in 'mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'imagenette160', 'imagenette320', 'imagenette', 'imagewoof160', 'imagewoof320', 'imagewoof':\n",
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" !python classify/train.py --model {m} --data {d} --epochs 10 --project YOLOv5-cls --name {m}-{d}"
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],
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