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tutorial.ipynb

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