Created using Colaboratory

tutorial.ipynb

@@ -954,7 +954,7 @@
       "source": [
         "# Appendix\n",
         "\n",
-        "Additional content below for PyTorch Hub, CI, reproducing results, profiling speeds, VOC training, classification training and TensorRT example."
+        "Additional content below."
       ]
     },
     {
@@ -963,145 +963,16 @@
         "id": "GMusP4OAxFu6"
       },
       "source": [
+        "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
         "import torch\n",
         "\n",
-        "# PyTorch Hub Model\n",
-        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom\n",
-        "\n",
-        "# Images\n",
-        "img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list\n",
-        "\n",
-        "# Inference\n",
-        "results = model(img)\n",
-        "\n",
-        "# Results\n",
+        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # yolov5n - yolov5x6 or custom\n",
+        "im = 'https://ultralytics.com/images/zidane.jpg'  # file, Path, PIL.Image, OpenCV, nparray, list\n",
+        "results = model(im)  # inference\n",
         "results.print()  # or .show(), .save(), .crop(), .pandas(), etc."
       ],
       "execution_count": null,
       "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FGH0ZjkGjejy"
-      },
-      "source": [
-        "# YOLOv5 CI\n",
-        "%%shell\n",
-        "rm -rf runs  # remove runs/\n",
-        "m=yolov5n  # official weights\n",
-        "b=runs/train/exp/weights/best  # best.pt checkpoint\n",
-        "python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device 0  # train\n",
-        "for d in 0 cpu; do  # devices\n",
-        "  for w in $m $b; do  # weights\n",
-        "    python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d  # val\n",
-        "    python detect.py --imgsz 64 --weights $w.pt --device $d  # detect\n",
-        "  done\n",
-        "done\n",
-        "python hubconf.py --model $m  # hub\n",
-        "python models/tf.py --weights $m.pt  # build TF model\n",
-        "python models/yolo.py --cfg $m.yaml  # build PyTorch model\n",
-        "python export.py --weights $m.pt --img 64 --include torchscript  # export"
-      ],
-      "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": {
-        "id": "gogI-kwi3Tye"
-      },
-      "source": [
-        "# Profile\n",
-        "from utils.torch_utils import profile\n",
-        "\n",
-        "m1 = lambda x: x * torch.sigmoid(x)\n",
-        "m2 = torch.nn.SiLU()\n",
-        "results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "BSgFCAcMbk1R"
-      },
-      "source": [
-        "# 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",
-      "source": [
-        "# 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}"
-      ],
-      "metadata": {
-        "id": "UWGH7H6yakVl"
-      },
-      "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",
-      "source": [
-        "# Validate on COCO test. Zip results.json and submit to eval server at https://competitions.codalab.org/competitions/20794\n",
-        "!bash data/scripts/get_coco.sh --test  # download COCO test-dev2017 (7G - 40000 images, test 20000)\n",
-        "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half --task test"
-      ],
-      "metadata": {
-        "id": "aq4DPWGu0Bl1"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "VTRwsvA9u7ln"
-      },
-      "source": [
-        "# TensorRT \n",
-        "!pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com  # install\n",
-        "!python export.py --weights yolov5s.pt --include engine --imgsz 640 --device 0  # export\n",
-        "!python detect.py --weights yolov5s.engine --imgsz 640 --device 0  # inference"
-      ],
-      "execution_count": null,
-      "outputs": []
     }
   ]
-}
+}
\ No newline at end of file