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tutorial.ipynb

@@ -1014,23 +1014,9 @@
       "source": [
         "# Appendix\n",
         "\n",
-        "Optional extras below. Unit tests validate repo functionality and should be run on any PRs submitted.\n"
+        "Additional content below for PyTorch Hub, CI, reproducing results, profiling speeds, VOC training, classification training and TensorRT example."
       ]
     },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "mcKoSIK2WSzj"
-      },
-      "source": [
-        "# Reproduce\n",
-        "for x in 'yolov5n', 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x':\n",
-        "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed  # speed\n",
-        "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65  # mAP"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "code",
       "metadata": {
@@ -1080,6 +1066,20 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mcKoSIK2WSzj"
+      },
+      "source": [
+        "# Reproduce\n",
+        "for x in (f'yolov5{x}' for x in 'nsmlx'):\n",
+        "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed  # speed\n",
+        "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65  # mAP"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "metadata": {
@@ -1099,26 +1099,27 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "RVRSOhEvUdb5"
+        "id": "BSgFCAcMbk1R"
       },
       "source": [
-        "# Evolve\n",
-        "!python train.py --img 640 --batch 64 --epochs 100 --data coco128.yaml --weights yolov5s.pt --cache --noautoanchor --evolve\n",
-        "!d=runs/train/evolve && cp evolve.* $d && zip -r evolve.zip $d && gsutil mv evolve.zip gs://bucket  # upload results (optional)"
+        "# VOC\n",
+        "for b, m in zip([64, 64, 64, 32, 16], [f'yolov5{x}' for x in 'nsmlx']):  # batch, model\n",
+        "  !python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.VOC.yaml --project VOC --name {m} --cache"
       ],
       "execution_count": null,
       "outputs": []
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "BSgFCAcMbk1R"
-      },
       "source": [
-        "# VOC\n",
-        "for b, m in zip([64, 64, 64, 32, 16], ['yolov5n', 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']):  # batch, model\n",
-        "  !python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.VOC.yaml --project VOC --name {m} --cache"
+        "# Classification\n",
+        "for m in *(f'yolov5{x}.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}"
       ],
+      "metadata": {
+        "id": "UWGH7H6yakVl"
+      },
       "execution_count": null,
       "outputs": []
     },