Created using Colaboratory

4e65052f · Glenn Jocher · 01cdb767 · 4e65052f
Commit 4e65052f authored Aug 15, 2021 by Glenn Jocher
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -6,7 +6,6 @@
      "name": "YOLOv5 Tutorial",
      "provenance": [],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
-        "outputId": "ada1dd8d-e0aa-4858-e893-dc320319ca30"
+        "outputId": "4d67116a-43e9-4d84-d19e-1edd83f23a04"
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
@@ -580,7 +415,7 @@
        "clear_output()\n",
        "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")"
      ],
-      "execution_count": null,
+      "execution_count": 1,
      "outputs": [
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@@ -619,25 +454,26 @@
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
-        "outputId": "a7a37616-a82b-4bdb-a463-6ead850b5615"
+        "outputId": "8b728908-81ab-4861-edb0-4d0c46c439fb"
      },
      "source": [
+        "%rm -rf runs\n",
        "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",
-        "Image(filename='runs/detect/exp/zidane.jpg', width=600)"
+        "#Image(filename='runs/detect/exp/zidane.jpg', width=600)"
      ],
-      "execution_count": null,
+      "execution_count": 4,
      "outputs": [
        {
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          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images/, imgsz=640, conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False\n",
-            "YOLOv5 🚀 v5.0-330-g18f6ba7 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "YOLOv5 🚀 v5.0-367-g01cdb76 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 224 layers, 7266973 parameters, 0 gradients\n",
-            "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 1 fire hydrant, Done. (0.008s)\n",
+            "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 1 fire hydrant, Done. (0.007s)\n",
-            "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.008s)\n",
+            "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.007s)\n",
-            "Results saved to runs/detect/exp\n",
+            "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n",
            "Done. (0.091s)\n"
          ],
          "name": "stdout"
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      "source": [
        "# Download COCO val2017\n",
        "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n",
        "!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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+        "outputId": "3dd0e2fc-aecf-4108-91b1-6392da1863cb"
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      "source": [
        "# Run YOLOv5x on COCO val2017\n",
        "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
      ],
-      "execution_count": null,
+      "execution_count": 6,
      "outputs": [
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            "\u001b[34m\u001b[1mval: \u001b[0mdata=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True\n",
-            "YOLOv5 🚀 v5.0-330-g18f6ba7 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "YOLOv5 🚀 v5.0-367-g01cdb76 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n",
-            "100% 168M/168M [00:05<00:00, 31.9MB/s]\n",
+            "100% 168M/168M [00:08<00:00, 20.6MB/s]\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 476 layers, 87730285 parameters, 0 gradients\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2653.03it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2749.96it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../datasets/coco/val2017.cache\n",
-            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [01:18<00:00,  2.00it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [01:08<00:00,  2.28it/s]\n",
            "                 all       5000      36335      0.746      0.626       0.68       0.49\n",
-            "Speed: 0.1ms pre-process, 5.1ms inference, 1.5ms NMS per image at shape (32, 3, 640, 640)\n",
+            "Speed: 0.1ms pre-process, 5.1ms inference, 1.6ms NMS per image at shape (32, 3, 640, 640)\n",
            "\n",
            "Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n",
            "loading annotations into memory...\n",
-            "Done (t=0.44s)\n",
+            "Done (t=0.46s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
-            "DONE (t=4.82s)\n",
+            "DONE (t=4.94s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
-            "DONE (t=84.52s).\n",
+            "DONE (t=83.60s).\n",
            "Accumulating evaluation results...\n",
-            "DONE (t=13.82s).\n",
+            "DONE (t=13.22s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.688\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.546\n",
@@ -784,7 +616,7 @@
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524\n",