Created using Colaboratory

tutorial.ipynb

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       "name": "YOLOv5 Tutorial",
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         "colab": {
           "base_uri": "https://localhost:8080/"
         },
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+        "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,
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         "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 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.008s)\n",
-            "Results saved to runs/detect/exp\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. (0.007s)\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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           "text": [
             "\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",
             " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735\n",
             " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.827\n",
-            "Results saved to runs/val/exp\n"
+            "Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
           ],
           "name": "stdout"
         }
@@ -841,54 +673,15 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "Knxi2ncxWffW",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 66,
-          "referenced_widgets": [
-            "6ff8a710ded44391a624dec5c460b771",
-            "3c19729b51cd45d4848035da06e96ff8",
-            "23b2f0ae3d46438c8de375987c77f580",
-            "dd9498c321a9422da6faf17a0be026d4",
-            "d8dda4b2ce864fd682e558b9a48f602e",
-            "ff8151449e444a14869684212b9ab14e",
-            "0f84fe609bcf4aa9afdc32a8cf076909",
-            "8fda673769984e2b928ef820d34c85c3"
-          ]
-        },
-        "outputId": "4510c6b0-8d2a-436c-d3f4-c8f8470d913a"
+        "id": "Knxi2ncxWffW"
       },
       "source": [
         "# Download COCO128\n",
         "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n",
-        "!unzip -q tmp.zip -d ../ && rm tmp.zip"
+        "!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "6ff8a710ded44391a624dec5c460b771",
-              "version_minor": 0,
-              "version_major": 2
-            },
-            "text/plain": [
-              "HBox(children=(FloatProgress(value=0.0, max=6984509.0), HTML(value='')))"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -935,40 +728,34 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "cd8ac17d-19a8-4e87-ab6a-31af1edac1ef"
+        "outputId": "00ea4b14-a75c-44a2-a913-03b431b69de5"
       },
       "source": [
         "# 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": null,
+      "execution_count": 8,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache_images=True, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, entity=None, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias=latest, local_rank=-1\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, entity=None, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias=latest, local_rank=-1, freeze=0\n",
             "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\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",
             "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
             "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)\n",
             "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
-            "2021-07-29 22:56:52.096481: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
-            "\n",
-            "WARNING: Dataset not found, nonexistent paths: ['/content/datasets/coco128/images/train2017']\n",
-            "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip ...\n",
-            "100% 6.66M/6.66M [00:00<00:00, 44.0MB/s]\n",
-            "Dataset autodownload success\n",
-            "\n",
+            "2021-08-15 14:40:43.449642: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
             "\n",
             "                 from  n    params  module                                  arguments                     \n",
             "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \n",
             "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
             "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
             "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
-            "  4                -1  1    156928  models.common.C3                        [128, 128, 3]                 \n",
+            "  4                -1  3    156928  models.common.C3                        [128, 128, 3]                 \n",
             "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
-            "  6                -1  1    625152  models.common.C3                        [256, 256, 3]                 \n",
+            "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
             "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
             "  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        \n",
             "  9                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
@@ -993,11 +780,11 @@
             "Scaled weight_decay = 0.0005\n",
             "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 59 weight, 62 weight (no decay), 62 bias\n",
             "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n",
-            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2021.98it/s]\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2440.28it/s]\n",
             "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../datasets/coco128/labels/train2017.cache\n",
-            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 273.58it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 506004.63it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:01<00:00, 121.71it/s]\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 302.61it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<?, ?it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 142.55it/s]\n",
             "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
             "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
             "Plotting labels... \n",
@@ -1009,23 +796,24 @@
             "Starting training for 3 epochs...\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
-            "       0/2     3.64G    0.0441   0.06646   0.02229       290       640: 100% 8/8 [00:04<00:00,  1.93it/s]\n",
-            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00,  3.45it/s]\n",
-            "                 all        128        929      0.696      0.562      0.644      0.419\n",
+            "       0/2     3.64G   0.04492    0.0674   0.02213       298       640: 100% 8/8 [00:03<00:00,  2.05it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.70it/s]\n",
+            "                 all        128        929      0.686      0.565      0.642      0.421\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
-            "       1/2     5.04G   0.04573   0.06289     0.021       226       640: 100% 8/8 [00:01<00:00,  5.46it/s]\n",
-            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00,  3.16it/s]\n",
-            "                 all        128        929       0.71      0.567      0.654      0.424\n",
+            "       1/2     5.04G   0.04403    0.0611   0.01986       232       640: 100% 8/8 [00:01<00:00,  5.59it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.46it/s]\n",
+            "                 all        128        929      0.694      0.563      0.654      0.425\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
-            "       2/2     5.04G   0.04542    0.0715   0.02028       242       640: 100% 8/8 [00:01<00:00,  5.12it/s]\n",
-            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00,  1.46it/s]\n",
-            "                 all        128        929      0.731      0.563      0.658      0.427\n",
-            "3 epochs completed in 0.006 hours.\n",
+            "       2/2     5.04G   0.04616   0.07056   0.02071       214       640: 100% 8/8 [00:01<00:00,  5.94it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00,  1.52it/s]\n",
+            "                 all        128        929      0.711      0.562       0.66      0.431\n",
             "\n",
+            "3 epochs completed in 0.005 hours.\n",
             "Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",
-            "Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n"
+            "Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n",
+            "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
           ],
           "name": "stdout"
         }