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tutorial.ipynb

@@ -1112,8 +1112,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Classification\n",
-        "for m in [*(f'yolov5{x}-cls.pt' for x in 'nsmlx'), 'resnet50.pt', 'efficientnet_b0.pt']:\n",
+        "# Classification train\n",
+        "for m in [*(f'yolov5{x}-cls.pt' for x in 'nsmlx'), 'resnet50.pt', 'resnet101.pt', 'efficientnet_b0.pt', 'efficientnet_b1.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}"
       ],
@@ -1123,6 +1123,19 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Classification val\n",
+        "!bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)\n",
+        "!python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate"
+      ],
+      "metadata": {
+        "id": "yYgOiFNHZx-1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "metadata": {