{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "YOLOv5 Tutorial",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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 contains software 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 and RESTART RUNTIME."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbvMlHd_QwMG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
        "!pip install -U -r yolov5/requirements.txt  # install dependencies and RESTART RUNTIME"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3t8VD3WebfnI",
        "colab_type": "text"
      },
      "source": [
        "After restarting runtime, `%cd` back into `./yolov5` folder, import `torch` and check CUDA device."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5TuwHPkKGY1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "e2648183-73d0-49e2-8361-7c8d65a1cfe8"
      },
      "source": [
        "%cd yolov5"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/yolov5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ij5L0B5FXA6g",
        "colab_type": "code",
        "outputId": "6ab14e8e-392b-48ed-c77d-167f5a9d4a13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "import torch\n",
        "from IPython.display import Image  # for displaying images\n",
        "from utils.google_utils import gdrive_download  # for downloading models/datasets\n",
        "\n",
        "print('torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch 1.5.0+cu101 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', major=6, minor=0, total_memory=16280MB, multi_processor_count=56)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gI6NoBev8Ib1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Re-clone (only if necessary, not normally used)\n",
        "%cd ..\n",
        "!rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n",
        "%cd yolov5"
      ],
      "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": "9ae9f2bb-6cf6-4573-c755-1f5d194230c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "source": [
        "# Run inference\n",
        "!python detect.py --weights yolov5s.pt --img 640 --conf 0.4 --source ./inference/images/\n",
        "Image(filename='inference/output/zidane.jpg', width=600)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, 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: 640x512 3 persons, 1 buss, Done. (0.009s)\n",
            "image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)\n",
            "Results saved to /content/yolov5/inference/output\n",
            "Done. (0.118s)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": "[encoded data, 342228 characters]\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 600
            }
          },
          "execution_count": 4
        }
      ]
    },
    {
      "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",
        "outputId": "6948ef69-40d6-434e-c5ba-cc67605a04cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# Download COCO val2017\n",
        "gdrive_download('1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43','coco2017val.zip')  # val2017 dataset\n",
        "!mv ./coco ../  # move folder alongside /yolov5"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://drive.google.com/uc?export=download&id=1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43 as coco2017val.zip... unzipping... Done (25.8s)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LLb8HhnP1awM",
        "colab_type": "code",
        "outputId": "279038a2-18af-46a9-ad12-9c3aeaf2fc33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "# Run yolov5s on COCO val2017\n",
        "!python test.py --weights yolov5s.pt --data ./data/coco.yaml"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(augment=False, batch_size=16, 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 P100-PCIE-16GB', total_memory=16280MB)\n",
            "\n",
            "Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (1.9s)\n",
            "Model Summary: 99 layers, 6.99302e+06 parameters, 6.99302e+06 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, 23176.56it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 313/313 [01:41<00:00,  3.07it/s]\n",
            "                 all       5e+03    3.63e+04       0.317       0.621        0.52       0.314\n",
            "Speed: 3.1/2.8/5.8 ms inference/NMS/total per 640x640 image at batch-size 16\n",
            "\n",
            "COCO mAP with pycocotools... saving detections_val2017_yolov5s_results.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.40s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=13.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=130.84s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=24.98s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.330\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.528\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.350\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.374\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.422\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.282\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.482\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.558\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.397\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 ] = 0.691\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcJEsCk3PgQy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Run multiple models on COCO val2017\n",
        "%%shell\n",
        "for x in yolov5s yolov5m yolov5l yolov5x yolov3-spp\n",
        "do \n",
        "  python test.py --weights $x.pt --data ./data/coco.yaml\n",
        "done"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rc_KbFk0juX2",
        "colab_type": "text"
      },
      "source": [
        "###2.2 test-dev2017\n",
        "Download COCO test2017 dataset, 7GB, 40,000 images, to test model accuracy on test-dev set, 20,000 images. Results are saved to a `*.json` file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0AJnSeCIHyJ",
        "colab_type": "code",
        "outputId": "de02afec-83ce-4be1-d007-65f773947ada",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        }
      },
      "source": [
        "# Download COCO test-dev2017\n",
        "gdrive_download('1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L','coco2017labels.zip')  # annotations\n",
        "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$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": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://drive.google.com/uc?export=download&id=1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L as coco2017labels.zip... unzipping... Done (9.3s)\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 6339M  100 6339M    0     0  16.2M      0  0:06:30  0:06:30 --:--:-- 16.3M\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29GJXAP_lPrt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Run yolov5s on COCO test-dev2017\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": "b842bd43-67e3-4e0e-951a-7d80f3a6aa49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# Download tutorial dataset coco128.yaml\n",
        "gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip')  # tutorial dataset\n",
        "!mv ./coco128 ../  # move folder alongside /yolov5"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://drive.google.com/uc?export=download&id=1n_oKgR81BJtqk75b00eAjdv03qVCQn2f as coco128.zip... unzipping... Done (4.8s)\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 ''`\n",
        "\n",
        "Models 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": "0a1d64d9-b3a6-4055-8487-9741c89c1140",
        "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 '' --name yolov5s_coco128 --nosave --cache"
      ],
      "execution_count": 62,
      "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], name='yolov5s_coco128', nosave=True, notest=False, rect=False, resume=False, single_cls=False, weights='')\n",
            "Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)\n",
            "\n",
            "2020-05-30 00:01:16.653829: 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  3    246912  models.common.Bottleneck                [128, 128]                    \n",
            "  5             -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6             -1  3    985344  models.common.Bottleneck                [256, 256]                    \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   1312256  models.common.Bottleneck                [512, 512]                    \n",
            " 10             -1  1   1312256  models.common.Bottleneck                [512, 512, False]             \n",
            " 11             -1  1    130815  torch.nn.modules.conv.Conv2d            [512, 255, 1, 1, 0]           \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    328448  models.common.Bottleneck                [256, 256, False]             \n",
            " 16             -1  1     65535  torch.nn.modules.conv.Conv2d            [256, 255, 1, 1, 0]           \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     82304  models.common.Bottleneck                [128, 128, False]             \n",
            " 21             -1  1     32895  torch.nn.modules.conv.Conv2d            [128, 255, 1, 1, 0]           \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: 99 layers, 6.99302e+06 parameters, 6.99302e+06 gradients\n",
            "\n",
            "Optimizer groups: 34 .bias, 34 conv.weight, 31 other\n",
            "Caching labels ../coco128/labels/train2017 (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 6595.06it/s]\n",
            "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 154.24it/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, 9849.04it/s]\n",
            "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 151.99it/s]\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     9.65G    0.1222   0.08038    0.1221    0.3248       223       640: 100% 8/8 [00:06<00:00,  1.14it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:03<00:00,  2.31it/s]\n",
            "                 all         128         929           0           0           0           0\n",
            "\n",
            "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
            "       1/4     8.86G    0.1163   0.08987    0.1211    0.3273       348       640: 100% 8/8 [00:02<00:00,  3.64it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:00<00:00,  8.80it/s]\n",
            "                 all         128         929           0           0           0           0\n",
            "\n",
            "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
            "       2/4     8.86G    0.1145   0.08071    0.1214    0.3166       187       640: 100% 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:00<00:00,  8.62it/s]\n",
            "                 all         128         929           0           0           0           0\n",
            "\n",
            "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
            "       3/4     8.86G     0.113   0.07988    0.1199    0.3128       236       640: 100% 8/8 [00:02<00:00,  3.53it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:00<00:00,  8.79it/s]\n",
            "                 all         128         929           0           0           0           0\n",
            "\n",
            "     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size\n",
            "       4/4     8.86G    0.1109   0.08885    0.1198    0.3196       257       640: 100% 8/8 [00:02<00:00,  3.62it/s]\n",
            "               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:00<00:00,  8.56it/s]\n",
            "                 all         128         929           0           0           0           0\n",
            "Optimizer stripped from weights/last_yolov5s_coco128.pt\n",
            "5 epochs completed in 0.007 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 train images"
      ],
      "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 (blue), and starting from pretrained `yolov5s.pt` weights (orange)."
      ]
    },
    {
      "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
        }
      ]
    }
  ]
}