From 62d77a10274e55134e0ef1cec258b4ed6446cd3d Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Wed, 3 Nov 2021 18:55:25 +0100
Subject: [PATCH] Created using Colaboratory

---
 tutorial.ipynb | 327 ++++++++++++++++++++++++++++++-------------------
 1 file changed, 199 insertions(+), 128 deletions(-)

diff --git a/tutorial.ipynb b/tutorial.ipynb
index 115d767a7..b81db0de6 100644
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -15,7 +15,7 @@
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@@ -28,16 +28,16 @@
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             "box_style": "",
-            "layout": "IPY_MODEL_78cceec059784f2bb36988d3336e4d56",
+            "layout": "IPY_MODEL_769ecde6f2e64bacb596ce972f8d3d2d",
             "_model_module": "@jupyter-widgets/controls",
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-              "IPY_MODEL_30df865ded4c434191bce772c9a82f3a",
-              "IPY_MODEL_20cdc61eb3404f42a12b37901b0d85fb"
+              "IPY_MODEL_384a001876054c93b0af45cd1e960bfe",
+              "IPY_MODEL_dded0aeae74440f7ba2ffa0beb8dd612",
+              "IPY_MODEL_5296d28be75740b2892ae421bbec3657"
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-        "78cceec059784f2bb36988d3336e4d56": {
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@@ -89,13 +89,13 @@
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           "model_name": "HTMLModel",
           "model_module_version": "1.5.0",
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-            "style": "IPY_MODEL_2d7239993a9645b09b221405ac682743",
+            "style": "IPY_MODEL_9f09facb2a6c4a7096810d327c8b551c",
             "_dom_classes": [],
             "description": "",
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@@ -107,16 +107,16 @@
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-            "layout": "IPY_MODEL_17b5a87f92104ec7ab96bf507637d0d2"
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-        "30df865ded4c434191bce772c9a82f3a": {
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-            "style": "IPY_MODEL_2358bfb2270247359e94b066b3cc3d1f",
+            "style": "IPY_MODEL_0ce7164fc0c74bb9a2b5c7037375a727",
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@@ -131,31 +131,31 @@
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-            "style": "IPY_MODEL_654d8a19b9f949c6bbdaf8b0875c931e",
+            "style": "IPY_MODEL_473371611126476c88d5d42ec7031ed6",
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             "_view_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "value": " 780M/780M [00:33&lt;00:00, 24.4MB/s]",
+            "value": " 780M/780M [00:11&lt;00:00, 91.9MB/s]",
             "_view_count": null,
             "_view_module_version": "1.5.0",
             "description_tooltip": null,
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-        "2d7239993a9645b09b221405ac682743": {
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@@ -170,7 +170,7 @@
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@@ -222,7 +222,7 @@
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@@ -238,7 +238,7 @@
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@@ -290,7 +290,7 @@
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-        "654d8a19b9f949c6bbdaf8b0875c931e": {
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@@ -305,7 +305,7 @@
             "_model_module": "@jupyter-widgets/controls"
           }
         },
-        "896030c5d13b415aaa05032818d81a6e": {
+        "65efdfd0d26c46e79c8c5ff3b77126cc": {
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@@ -402,7 +402,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "4d67116a-43e9-4d84-d19e-1edd83f23a04"
+        "outputId": "e2e839d5-d6fc-409c-e44c-0b0b6aa9319d"
       },
       "source": [
         "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
@@ -415,14 +415,14 @@
         "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": 11,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "Setup complete. Using torch 1.9.0+cu102 (Tesla V100-SXM2-16GB)\n"
-          ],
-          "name": "stdout"
+            "Setup complete. Using torch 1.10.0+cu102 (Tesla V100-SXM2-16GB)\n"
+          ]
         }
       ]
     },
@@ -454,28 +454,28 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "8b728908-81ab-4861-edb0-4d0c46c439fb"
+        "outputId": "8f7e6588-215d-4ebd-93af-88b871e770a7"
       },
       "source": [
-        "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\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)"
       ],
-      "execution_count": null,
+      "execution_count": 17,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "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 🚀 v5.0-367-g01cdb76 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, imgsz=[640, 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, dnn=False\n",
+            "YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\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. (0.007s)\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"
+            "Model Summary: 213 layers, 7225885 parameters, 0 gradients\n",
+            "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.007s)\n",
+            "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.007s)\n",
+            "Speed: 0.5ms pre-process, 6.9ms inference, 1.3ms NMS per image at shape (1, 3, 640, 640)\n",
+            "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
+          ]
         }
       ]
     },
@@ -517,33 +517,33 @@
           "base_uri": "https://localhost:8080/",
           "height": 48,
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-        "outputId": "7e6f5c96-c819-43e1-cd03-d3b9878cf8de"
+        "outputId": "bcf9a448-1f9b-4a41-ad49-12f181faf05a"
       },
       "source": [
         "# Download COCO val\n",
         "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n",
         "!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
       ],
-      "execution_count": null,
+      "execution_count": 18,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "484511f272e64eab8b42e68dac5f7a66",
+              "model_id": "eb95db7cae194218b3fcefb439b6352f",
               "version_minor": 0,
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@@ -551,9 +551,7 @@
               "  0%|          | 0.00/780M [00:00<?, ?B/s]"
             ]
           },
-          "metadata": {
-            "tags": []
-          }
+          "metadata": {}
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     },
@@ -564,30 +562,31 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "3dd0e2fc-aecf-4108-91b1-6392da1863cb"
+        "outputId": "74f1dfa9-6b6d-4b36-f67e-bbae243869f9"
       },
       "source": [
         "# Run YOLOv5x on COCO val\n",
         "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
       ],
-      "execution_count": null,
+      "execution_count": 19,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "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 🚀 v5.0-367-g01cdb76 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/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 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
             "\n",
-            "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n",
-            "100% 168M/168M [00:08<00:00, 20.6MB/s]\n",
+            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5x.pt to yolov5x.pt...\n",
+            "100% 166M/166M [00:03<00:00, 54.1MB/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, 2749.96it/s]\n",
+            "Model Summary: 444 layers, 86705005 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, 2636.64it/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 [01:08<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, 1.6ms NMS per image at shape (32, 3, 640, 640)\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [01:12<00:00,  2.17it/s]\n",
+            "                 all       5000      36335      0.729       0.63      0.683      0.496\n",
+            "Speed: 0.1ms pre-process, 4.9ms inference, 1.9ms 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",
@@ -595,29 +594,28 @@
             "creating index...\n",
             "index created!\n",
             "Loading and preparing results...\n",
-            "DONE (t=4.94s)\n",
+            "DONE (t=5.15s)\n",
             "creating index...\n",
             "index created!\n",
             "Running per image evaluation...\n",
             "Evaluate annotation type *bbox*\n",
-            "DONE (t=83.60s).\n",
+            "DONE (t=90.39s).\n",
             "Accumulating evaluation results...\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",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.382\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.629\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.681\n",
-            " 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",
+            "DONE (t=14.54s).\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.507\n",
+            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.689\n",
+            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.552\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.345\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.559\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.652\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.381\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.630\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.682\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.732\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829\n",
             "Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
-          ],
-          "name": "stdout"
+          ]
         }
       ]
     },
@@ -722,37 +720,37 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "00ea4b14-a75c-44a2-a913-03b431b69de5"
+        "outputId": "8724d13d-6711-4a12-d96a-1c655e5c3549"
       },
       "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": 24,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stdout",
           "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=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[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, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=0, save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
             "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
-            "YOLOv5 🚀 v5.0-367-g01cdb76 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\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[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, 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",
-            "2021-08-15 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",
+            "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \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  3    156928  models.common.C3                        [128, 128, 3]                 \n",
+            "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
             "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \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",
+            "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
+            "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
             " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
             " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
             " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
@@ -768,48 +766,121 @@
             " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
             " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
             " 24      [17, 20, 23]  1    229245  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
-            "Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPs\n",
+            "Model Summary: 270 layers, 7235389 parameters, 7235389 gradients, 16.5 GFLOPs\n",
             "\n",
-            "Transferred 362/362 items from yolov5s.pt\n",
+            "Transferred 349/349 items from yolov5s.pt\n",
             "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[1moptimizer:\u001b[0m SGD with parameter groups 57 weight, 60 weight (no decay), 60 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, 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 ram): 100% 128/128 [00:00<00:00, 302.61it/s]\n",
+            "\u001b[34m\u001b[1mtrain: \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[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 296.04it/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",
+            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 121.58it/s]\n",
             "Plotting labels... \n",
             "\n",
             "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.27, Best Possible Recall (BPR) = 0.9935\n",
             "Image sizes 640 train, 640 val\n",
             "Using 2 dataloader workers\n",
-            "Logging results to runs/train/exp\n",
+            "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
             "Starting training for 3 epochs...\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\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",
+            "       0/2     3.62G   0.04621    0.0711   0.02112       203       640: 100% 8/8 [00:04<00:00,  1.99it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.37it/s]\n",
+            "                 all        128        929      0.655      0.547      0.622       0.41\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\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",
+            "       1/2     5.31G   0.04564   0.06898   0.02116       143       640: 100% 8/8 [00:01<00:00,  4.77it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.27it/s]\n",
+            "                 all        128        929       0.68      0.554      0.632      0.419\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\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",
+            "       2/2     5.31G   0.04487   0.06883   0.01998       253       640: 100% 8/8 [00:01<00:00,  4.91it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.30it/s]\n",
+            "                 all        128        929       0.71      0.544      0.629      0.423\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",
+            "3 epochs completed in 0.003 hours.\n",
+            "Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB\n",
+            "Optimizer stripped from runs/train/exp/weights/best.pt, 14.9MB\n",
+            "\n",
+            "Validating runs/train/exp/weights/best.pt...\n",
+            "Fusing layers... \n",
+            "Model Summary: 213 layers, 7225885 parameters, 0 gradients, 16.5 GFLOPs\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00,  1.04it/s]\n",
+            "                 all        128        929       0.71      0.544       0.63      0.423\n",
+            "              person        128        254      0.816      0.669      0.774      0.507\n",
+            "             bicycle        128          6      0.799      0.667      0.614      0.371\n",
+            "                 car        128         46      0.803      0.355      0.486      0.209\n",
+            "          motorcycle        128          5      0.704        0.6      0.791      0.583\n",
+            "            airplane        128          6          1      0.795      0.995      0.717\n",
+            "                 bus        128          7      0.656      0.714       0.72      0.606\n",
+            "               train        128          3      0.852          1      0.995      0.682\n",
+            "               truck        128         12      0.521       0.25      0.395      0.215\n",
+            "                boat        128          6      0.795      0.333      0.445      0.137\n",
+            "       traffic light        128         14      0.576      0.143       0.24      0.161\n",
+            "           stop sign        128          2      0.636        0.5      0.828      0.713\n",
+            "               bench        128          9      0.972      0.444      0.575       0.25\n",
+            "                bird        128         16      0.939      0.968      0.988      0.645\n",
+            "                 cat        128          4      0.984       0.75      0.822      0.694\n",
+            "                 dog        128          9      0.888      0.667      0.903       0.54\n",
+            "               horse        128          2      0.689          1      0.995      0.697\n",
+            "            elephant        128         17       0.96      0.882      0.943      0.681\n",
+            "                bear        128          1      0.549          1      0.995      0.995\n",
+            "               zebra        128          4       0.86          1      0.995      0.952\n",
+            "             giraffe        128          9      0.822      0.778      0.905       0.57\n",
+            "            backpack        128          6          1      0.309      0.457      0.195\n",
+            "            umbrella        128         18      0.775      0.576       0.74      0.418\n",
+            "             handbag        128         19      0.628      0.105      0.167      0.111\n",
+            "                 tie        128          7       0.96      0.571      0.701      0.441\n",
+            "            suitcase        128          4          1      0.895      0.995      0.621\n",
+            "             frisbee        128          5      0.641        0.8      0.798      0.664\n",
+            "                skis        128          1      0.627          1      0.995      0.497\n",
+            "           snowboard        128          7      0.988      0.714      0.768      0.556\n",
+            "         sports ball        128          6      0.671        0.5      0.579      0.339\n",
+            "                kite        128         10      0.631      0.515      0.598      0.221\n",
+            "        baseball bat        128          4       0.47      0.456      0.277      0.137\n",
+            "      baseball glove        128          7      0.459      0.429      0.334      0.182\n",
+            "          skateboard        128          5        0.7       0.48      0.736      0.548\n",
+            "       tennis racket        128          7      0.559      0.571      0.538      0.315\n",
+            "              bottle        128         18      0.607      0.389      0.484      0.282\n",
+            "          wine glass        128         16      0.722      0.812       0.82      0.385\n",
+            "                 cup        128         36      0.881      0.361      0.532      0.312\n",
+            "                fork        128          6      0.384      0.167      0.239      0.191\n",
+            "               knife        128         16      0.908      0.616      0.681      0.443\n",
+            "               spoon        128         22      0.836      0.364      0.536      0.264\n",
+            "                bowl        128         28      0.793      0.536      0.633      0.471\n",
+            "              banana        128          1          0          0      0.142     0.0995\n",
+            "            sandwich        128          2          0          0     0.0951     0.0717\n",
+            "              orange        128          4          1          0       0.67      0.317\n",
+            "            broccoli        128         11      0.345      0.182      0.283      0.243\n",
+            "              carrot        128         24      0.688      0.459      0.612      0.402\n",
+            "             hot dog        128          2      0.424      0.771      0.497      0.473\n",
+            "               pizza        128          5      0.622          1      0.824      0.551\n",
+            "               donut        128         14      0.703          1      0.952      0.853\n",
+            "                cake        128          4      0.733          1      0.945      0.777\n",
+            "               chair        128         35      0.512      0.486      0.488      0.222\n",
+            "               couch        128          6       0.68       0.36      0.746      0.406\n",
+            "        potted plant        128         14      0.797      0.714      0.808      0.482\n",
+            "                 bed        128          3          1          0      0.474      0.318\n",
+            "        dining table        128         13      0.852      0.445      0.478      0.315\n",
+            "              toilet        128          2      0.512        0.5      0.554      0.487\n",
+            "                  tv        128          2      0.754          1      0.995      0.895\n",
+            "              laptop        128          3          1          0       0.39      0.147\n",
+            "               mouse        128          2          1          0     0.0283     0.0226\n",
+            "              remote        128          8      0.747      0.625      0.636      0.488\n",
+            "          cell phone        128          8      0.555      0.166      0.417      0.222\n",
+            "           microwave        128          3      0.417          1      0.995      0.732\n",
+            "                oven        128          5       0.37        0.4      0.432      0.249\n",
+            "                sink        128          6      0.356      0.167      0.269      0.149\n",
+            "        refrigerator        128          5      0.705        0.8      0.814       0.45\n",
+            "                book        128         29      0.628      0.138      0.298      0.136\n",
+            "               clock        128          9      0.857      0.778      0.893      0.574\n",
+            "                vase        128          2      0.242          1      0.663      0.622\n",
+            "            scissors        128          1          1          0     0.0207    0.00207\n",
+            "          teddy bear        128         21      0.847      0.381      0.622      0.345\n",
+            "          toothbrush        128          5       0.99        0.6      0.662       0.45\n",
             "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
-          ],
-          "name": "stdout"
+          ]
         }
       ]
     },
@@ -953,19 +1024,19 @@
         "%%shell\n",
         "export PYTHONPATH=\"$PWD\"  # to run *.py. files in subdirectories\n",
         "rm -rf runs  # remove runs/\n",
-        "for m in yolov5s; do  # models\n",
-        "  python train.py --weights $m.pt --epochs 3 --img 320 --device 0  # train pretrained\n",
-        "  python train.py --weights '' --cfg $m.yaml --epochs 3 --img 320 --device 0  # train scratch\n",
+        "for m in yolov5n; do  # models\n",
+        "  python train.py --img 64 --batch 32 --weights $m.pt --epochs 1 --device 0  # train pretrained\n",
+        "  python train.py --img 64 --batch 32 --weights '' --cfg $m.yaml --epochs 1 --device 0  # train scratch\n",
         "  for d in 0 cpu; do  # devices\n",
-        "    python detect.py --weights $m.pt --device $d  # detect official\n",
-        "    python detect.py --weights runs/train/exp/weights/best.pt --device $d  # detect custom\n",
         "    python val.py --weights $m.pt --device $d # val official\n",
         "    python val.py --weights runs/train/exp/weights/best.pt --device $d # val custom\n",
+        "    python detect.py --weights $m.pt --device $d  # detect official\n",
+        "    python detect.py --weights runs/train/exp/weights/best.pt --device $d  # detect custom\n",
         "  done\n",
-        "python hubconf.py  # hub\n",
-        "python models/yolo.py --cfg $m.yaml  # build PyTorch model\n",
-        "python models/tf.py --weights $m.pt  # build TensorFlow model\n",
-        "python export.py --img 128 --batch 1 --weights $m.pt --include torchscript onnx  # export\n",
+        "  python hubconf.py  # hub\n",
+        "  python models/yolo.py --cfg $m.yaml  # build PyTorch model\n",
+        "  python models/tf.py --weights $m.pt  # build TensorFlow model\n",
+        "  python export.py --img 64 --batch 1 --weights $m.pt --include torchscript onnx  # export\n",
         "done"
       ],
       "execution_count": null,
@@ -1014,4 +1085,4 @@
       "outputs": []
     }
   ]
-}
+}
\ No newline at end of file