{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "YOLOv5 Tutorial",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HvhYZrIZCEyo"
      },
      "source": [
        "<img src=\"https://user-images.githubusercontent.com/26833433/82952157-51b7db00-9f5d-11ea-8f4b-dda1ffecf992.jpg\">\n",
        "\n",
        "This notebook was developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mGmQbAO5pQb",
        "colab_type": "text"
      },
      "source": [
        "#Initial Setup\n",
        "\n",
        "Clone repo, install dependencies, `%cd` into `./yolov5` folder and check GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbvMlHd_QwMG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
        "!pip install -r yolov5/requirements.txt  # install dependencies\n",
        "%cd yolov5\n",
        "\n",
        "import torch\n",
        "from IPython.display import Image, clear_output  # to display images\n",
        "from utils.google_utils import gdrive_download  # to download models/datasets\n",
        "\n",
        "clear_output()\n",
        "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3qM6T0W53gh",
        "colab_type": "text"
      },
      "source": [
        "#1. Inference\n",
        "\n",
        "Run inference with a pretrained checkpoint on contents of `/inference/images` folder. Models are downloaded automatically from our Google Drive [folder](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) if available."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zR9ZbuQCH7FX",
        "colab_type": "code",
        "outputId": "528fcc04-2393-437a-84d2-092becbaefbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "source": [
        "!python detect.py --weights yolov5s.pt --img 416 --conf 0.4 --source ./inference/images/\n",
        "Image(filename='inference/output/zidane.jpg', width=600)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=416, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')\n",
            "Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)\n",
            "\n",
            "image 1/2 inference/images/bus.jpg: 416x352 3 persons, 1 buss, Done. (0.009s)\n",
            "image 2/2 inference/images/zidane.jpg: 288x416 2 persons, 2 ties, Done. (0.009s)\n",
            "Results saved to /content/yolov5/inference/output\n",
            "Done. (0.100s)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": "[encoded data, 339772 characters]\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 600
            }
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JnkELT0cIJg",
        "colab_type": "text"
      },
      "source": [
        "Inference can be run on a variety of sources: images, videos, directories, webcams, rtsp and http streams as shown in the example below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwosXmgDahte",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Example syntax (do not run cell)\n",
        "!python detect.py --source ./file.jpg  # image \n",
        "                           ./file.mp4  # video\n",
        "                           ./dir  # directory\n",
        "                           0  # webcam\n",
        "                           'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa' # rtsp\n",
        "                           'http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8'  # http"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eq1SMWl6Sfn",
        "colab_type": "text"
      },
      "source": [
        "#2. Test\n",
        "Test a model on COCO val or test-dev dataset to determine trained accuracy. Models are downloaded automatically from our Google Drive [folder](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) if available. To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be 1-2% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eyTZYGgRjnMc",
        "colab_type": "text"
      },
      "source": [
        "###2.1 val2017\n",
        "Download COCO val 2017 dataset, 1GB, 5000 images, and test model accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQPtK1QYVaD_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "e49d6899-0feb-4bc7-e384-513c7d384060"
      },
      "source": [
        "# Download COCO val2017\n",
        "gdrive_download('1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43','coco2017val.zip')  # val2017 dataset\n",
        "!mv ./coco ../  # move folder alongside /yolov5"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://drive.google.com/uc?export=download&id=1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43 as coco2017val.zip... unzipping... Done (37.1s)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBWS_QSc__se",
        "colab_type": "code",
        "outputId": "02b54431-b993-4acd-c8a3-9c3231662718",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        }
      },
      "source": [
        "# Run YOLOv5s on COCO val2017\n",
        "!python test.py --weights yolov5s.pt --data ./data/coco.yaml --img 640"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='././data/coco.yaml', device='', img_size=640, iou_thres=0.65, save_json=True, single_cls=False, task='val', verbose=False, weights='yolov5s.pt')\n",
            "Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)\n",
            "\n",
            "Model Summary: 165 layers, 7.07417e+06 parameters, 7.07417e+06 gradients\n",
            "Caching labels ../coco/val2017.txt (4952 found, 48 missing, 0 empty, 0 duplicate, for 5000 images): 100% 5000/5000 [00:00<00:00, 7822.09it/s]\n",
            "Saving labels to ../coco/labels/val2017.npy for faster future loading\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 157/157 [01:33<00:00,  1.67it/s]\n",
            "                 all       5e+03    3.63e+04       0.334       0.633       0.534       0.336\n",
            "Speed: 6.0/2.9/8.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
            "\n",
            "COCO mAP with pycocotools... saving detections_val2017_yolov5s_results.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.41s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=5.70s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=89.04s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=14.04s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.352\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.544\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.378\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.188\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.397\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.459\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.296\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.496\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.557\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.358\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.618\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X58w8JLpMnjH",
        "colab_type": "code",
        "outputId": "1cc32887-1fe3-4079-f66e-b6b756f96527",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        }
      },
      "source": [
        "# Run YOLOv5x on COCO val2017\n",
        "!python test.py --weights yolov5x.pt --data ./data/coco.yaml --img 640"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='././data/coco.yaml', device='', img_size=640, iou_thres=0.65, save_json=True, single_cls=False, task='val', verbose=False, weights='yolov5x.pt')\n",
            "Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)\n",
            "\n",
            "Model Summary: 381 layers, 9.59219e+07 parameters, 9.59219e+07 gradients\n",
            "Caching labels ../coco/labels/val2017.npy (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 100% 5000/5000 [00:00<00:00, 16516.13it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 157/157 [05:05<00:00,  1.95s/it]\n",
            "                 all       5e+03    3.63e+04       0.393       0.742       0.652       0.455\n",
            "Speed: 51.1/2.2/53.3 ms inference/NMS/total per 640x640 image at batch-size 32\n",
            "\n",
            "COCO mAP with pycocotools... saving detections_val2017_yolov5x_results.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=3.59s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=79.47s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=12.20s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.470\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.659\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.515\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.310\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.516\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.362\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.597\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.656\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.504\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.704\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.787\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rc_KbFk0juX2",
        "colab_type": "text"
      },
      "source": [
        "###2.2 test-dev2017\n",
        "Download COCO test2017 dataset, 7GB, 40,000 images, to test model accuracy on test-dev set, 20,000 images. Results are saved to a `*.json` file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0AJnSeCIHyJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Download COCO test-dev2017\n",
        "gdrive_download('1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L','coco2017labels.zip')  # annotations\n",
        "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f  # 7GB,  41k images\n",
        "!mv ./test2017 ./coco/images && mv ./coco ../  # move images into /coco and move /coco alongside /yolov5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29GJXAP_lPrt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Run YOLOv5s on COCO test-dev2017 with argument --task test\n",
        "!python test.py --weights yolov5s.pt --data ./data/coco.yaml --task test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUOiNLtMP5aG",
        "colab_type": "text"
      },
      "source": [
        "# 3. Train\n",
        "\n",
        "Download the 128-image tutorial training dataset `./data/coco128.yaml`, start tensorboard and train a `yolov5s.yaml` model for **5 epochs**. Note that actual training is typically much longer, around **300-1000 epochs**, depending on your dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Knxi2ncxWffW",
        "colab_type": "code",
        "outputId": "cbb574a9-861d-4f47-ca0c-32166bd3361e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# Download tutorial dataset coco128.yaml\n",
        "gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip')  # tutorial dataset\n",
        "!mv ./coco128 ../  # move folder alongside /yolov5"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://drive.google.com/uc?export=download&id=1n_oKgR81BJtqk75b00eAjdv03qVCQn2f as coco128.zip... unzipping... Done (5.8s)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOy5KI2ncnWd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Start tensorboard\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir runs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pOkGLv1dMqh",
        "colab_type": "text"
      },
      "source": [
        "Train a YOLOv5s model on the coco128 dataset by specifying model configuration file `--cfg ./models/yolo5s.yaml`, and a dataset configuration file `--data ./data/coco128.yaml`. Start training from pretrained `--weights yolov5s.pt`, or from scratch (randomly initialized weights) using `--weights ''`. Pretrained checkpoints are downloaded automatically from our Google Drive [folder](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) if available.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NcFxRcFdJ_O",
        "colab_type": "code",
        "outputId": "836944be-b677-4614-889b-da120cda0304",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Train YOLOv5s on coco128 for 5 epochs\n",
        "!python train.py --img 640 --batch 16 --epochs 5 --data ./data/coco128.yaml --cfg ./models/yolov5s.yaml --weights yolov5s.pt --name tutorial --nosave --cache"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex\n",
            "{'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.58, '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': 0.014, 'hsv_s': 0.68, 'hsv_v': 0.36, 'degrees': 0.0, 'translate': 0.0, 'scale': 0.5, 'shear': 0.0}\n",
            "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='././models/yolov5s.yaml', data='././data/coco128.yaml', device='', epochs=5, evolve=False, img_size=[640], multi_scale=False, name='tutorial', nosave=True, notest=False, rect=False, resume=False, single_cls=False, weights='yolov5s.pt')\n",
            "Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)\n",
            "\n",
            "2020-06-14 19:45:19.668043: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "Start Tensorboard with \"tensorboard --logdir=runs\", view at http://localhost:6006/\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     20672  models.common.Bottleneck                [64, 64]                      \n",
            "  3             -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4             -1  1    161152  models.common.BottleneckCSP             [128, 128, 3]                 \n",
            "  5             -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6             -1  1    641792  models.common.BottleneckCSP             [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   1905152  models.common.BottleneckCSP             [512, 512, 2]                 \n",
            " 10             -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          \n",
            " 11             -1  1    130815  torch.nn.modules.conv.Conv2d            [512, 255, 1, 1]              \n",
            " 12             -2  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 13        [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 14             -1  1    197120  models.common.Conv                      [768, 256, 1, 1]              \n",
            " 15             -1  1    313088  models.common.BottleneckCSP             [256, 256, 1, False]          \n",
            " 16             -1  1     65535  torch.nn.modules.conv.Conv2d            [256, 255, 1, 1]              \n",
            " 17             -2  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 18        [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 19             -1  1     49408  models.common.Conv                      [384, 128, 1, 1]              \n",
            " 20             -1  1     78720  models.common.BottleneckCSP             [128, 128, 1, False]          \n",
            " 21             -1  1     32895  torch.nn.modules.conv.Conv2d            [128, 255, 1, 1]              \n",
            " 22   [-1, 16, 11]  1         0  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]]\n",
            "Model Summary: 165 layers, 7.07417e+06 parameters, 7.07417e+06 gradients\n",
            "\n",
            "Optimizer groups: 54 .bias, 60 conv.weight, 51 other\n",
            "Reading image shapes: 100% 128/128 [00:00<00:00, 8158.02it/s]\n",
            "Caching labels ../coco128/labels/train2017 (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 7608.18it/s]\n",
            "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 149.70it/s]\n",
            "Caching labels ../coco128/labels/train2017 (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 8165.09it/s]\n",
            "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 145.34it/s]\n",
            "Label width-height:         n      mean       min       max  matching    recall\n",
            "                          929     105.8      1.23       640    0.4736    0.9903\n",
            "Image sizes 640 train, 640 test\n",
            "Using 2 dataloader workers\n",
            "Starting training for 5 epochs...\n",
            "\n",
            "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
            "       0/4      7.3G   0.04362   0.07954   0.02032    0.1435       208       640: 100% 8/8 [00:09<00:00,  1.24s/it]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:07<00:00,  1.03it/s]\n",
            "                 all         128         929       0.421       0.704        0.66        0.42\n",
            "\n",
            "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
            "       1/4     13.9G   0.04609   0.07819   0.01886    0.1431       167       640: 100% 8/8 [00:03<00:00,  2.34it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:02<00:00,  3.34it/s]\n",
            "                 all         128         929       0.418       0.713       0.659       0.421\n",
            "\n",
            "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
            "       2/4     13.9G   0.04508   0.07012   0.01977     0.135       198       640: 100% 8/8 [00:03<00:00,  2.33it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:02<00:00,  3.50it/s]\n",
            "                 all         128         929       0.409        0.71       0.664       0.421\n",
            "\n",
            "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
            "       3/4     13.9G   0.04577   0.08308   0.02038    0.1492       222       640: 100% 8/8 [00:03<00:00,  2.34it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:02<00:00,  3.50it/s]\n",
            "                 all         128         929       0.429        0.71        0.67       0.426\n",
            "\n",
            "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
            "       4/4     13.9G   0.04608   0.07352   0.01855    0.1382       149       640: 100% 8/8 [00:03<00:00,  2.32it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:02<00:00,  3.46it/s]\n",
            "                 all         128         929       0.423       0.716       0.663       0.422\n",
            "Optimizer stripped from weights/last_tutorial.pt\n",
            "5 epochs completed in 0.012 hours.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLI1JmHU7B0l",
        "colab_type": "text"
      },
      "source": [
        "#4. Visualize\n",
        "\n",
        "After training starts, view `train*.jpg` images to see training images, labels and augmentation effects. Note a mosaic dataloader is used for training (shown below), a new dataloading concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W40tI99_7BcH",
        "colab_type": "code",
        "outputId": "1c838e44-79fe-433f-a334-59a037ee322e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "Image(filename='./train_batch1.jpg', width=900)  # view augmented training mosaics"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": "[encoded data, 1056892 characters]\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 900
            }
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7uOowJKI-Qak",
        "colab_type": "text"
      },
      "source": [
        "View `test_batch0_gt.jpg` to see test batch 0 ground truth labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PF9MLHDb7tB6",
        "colab_type": "code",
        "outputId": "b7a874f7-dad3-4611-e777-56c724c7ee81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        }
      },
      "source": [
        "Image(filename='./test_batch0_gt.jpg', width=900)  # view test image labels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": "[encoded data, 751160 characters]\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 900
            }
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGrb16Mu-jif",
        "colab_type": "text"
      },
      "source": [
        "View `test_batch0_pred.jpg` to see test batch 0 predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycP4UTEZ82_I",
        "colab_type": "code",
        "outputId": "c7c1238d-e0fa-4fc5-f393-bf5bce55d245",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        }
      },
      "source": [
        "Image(filename='./test_batch0_pred.jpg', width=900)  # view test image predictions"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": "[encoded data, 753584 characters]\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 900
            }
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KN5ghjE6ZWh",
        "colab_type": "text"
      },
      "source": [
        "Training losses and performance metrics are saved to Tensorboard and also to a `results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.utils import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 100 epochs, starting from scratch (orange), and starting from pretrained `yolov5s.pt` weights (blue)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C60XAsyv6OPe",
        "colab_type": "code",
        "outputId": "70c2254e-9caf-46b0-afbe-f7fe1967b19b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "source": [
        "from utils.utils import plot_results; plot_results()  # plot results.txt as results.png\n",
        "Image(filename='./results.png', width=1000)  # view results.png"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "[encoded data, 328012 characters]\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 1000
            }
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEijrePND_2I",
        "colab_type": "text"
      },
      "source": [
        "## 5. Appendix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gI6NoBev8Ib1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Re-clone\n",
        "%cd ..\n",
        "!rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n",
        "%cd yolov5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OGhWlyAYQlS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Apex install\n",
        "git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" . --user && cd .. && rm -rf apex"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JaTFNJHvFBy4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Test GCP checkpoint on COCO val2017\n",
        "%%shell\n",
        "x=best*.pt\n",
        "gsutil cp gs://*/*/weights/$x .\n",
        "python test.py --weights $x --data ./data/coco.yaml --img 736"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuVlYvfBVTuD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Test multiple models on COCO val2017\n",
        "%%shell\n",
        "for x in yolov5s yolov5m yolov5l yolov5x\n",
        "do \n",
        "  python test.py --weights $x.pt --data ./data/coco.yaml --img 640 --conf 0.001\n",
        "done"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}