yolov7_custom_data_od.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "yolov7_custom_data.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "#mount drive \n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "woIUQHScnUCy",
        "outputId": "c6d0e845-8492-4b12-ebb2-2a6b6a2f36e5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/drive/MyDrive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TSmZdB_1q5g6",
        "outputId": "6756ebfc-9a03-4afd-d4d6-65b4a5dbfe92"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Errno 107] Transport endpoint is not connected: '/content/drive/MyDrive'\n",
            "/content/drive/MyDrive/yolov7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#clone repo yolov7\n",
        "!git clone https://github.com/WongKinYiu/yolov7.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jtZwXh1rB2-",
        "outputId": "57f94a15-ab5e-4b7b-f67e-2c567e0cbe6c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov7'...\n",
            "remote: Enumerating objects: 231, done.\u001b[K\n",
            "remote: Counting objects: 100% (66/66), done.\u001b[K\n",
            "remote: Compressing objects: 100% (56/56), done.\u001b[K\n",
            "remote: Total 231 (delta 38), reused 24 (delta 10), pack-reused 165\u001b[K\n",
            "Receiving objects: 100% (231/231), 14.98 MiB | 11.49 MiB/s, done.\n",
            "Resolving deltas: 100% (94/94), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/gdrive/MyDrive/yolov7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_C4nY6v1akE",
        "outputId": "73f7dc4c-7e12-4f60-9cd2-c507f421e832"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/yolov7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# download uyolov7.pth weight\n",
        "!wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-siiWXirEZq",
        "outputId": "017dec3a-1ee2-447d-9c68-f447cd20e423"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-07-11 05:57:46--  https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/b0243edf-9fb0-4337-95e1-42555f1b37cf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220711%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220711T055747Z&X-Amz-Expires=300&X-Amz-Signature=864d06f58b0b4fcb12b8efbae5bf9a0a470ddde08efe19da138080d047688045&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7.pt&response-content-type=application%2Foctet-stream [following]\n",
            "--2022-07-11 05:57:47--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/b0243edf-9fb0-4337-95e1-42555f1b37cf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220711%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220711T055747Z&X-Amz-Expires=300&X-Amz-Signature=864d06f58b0b4fcb12b8efbae5bf9a0a470ddde08efe19da138080d047688045&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7.pt&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 75587165 (72M) [application/octet-stream]\n",
            "Saving to: ‘yolov7.pt’\n",
            "\n",
            "yolov7.pt           100%[===================>]  72.08M  46.4MB/s    in 1.6s    \n",
            "\n",
            "2022-07-11 05:57:48 (46.4 MB/s) - ‘yolov7.pt’ saved [75587165/75587165]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zhzCjZwo5n6F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install PyYAML==5.4.1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Au9EQi0x5REy",
        "outputId": "8bd60347-c6a1-41a6-9496-ab40ec6db157"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: PyYAML==5.4.1 in /usr/local/lib/python3.7/dist-packages (5.4.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python train.py --workers 8 --device 0 --batch-size 16 --data data/custom_data.yaml --img 640 640 --cfg cfg/training/yolov7_custom_3.yaml --weights '' --name yolov7xcustom3 --hyp data/hyp.scratch.p5.yaml --epochs 200"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDp0dHRO1URX",
        "outputId": "b0d6f177-83f7-4b41-d878-3e668bed0ce2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "YOLOR 🚀 v0.1-16-ge44853e torch 1.11.0+cu113 CUDA:0 (Tesla T4, 15109.75MB)\n",
            "\n",
            "Namespace(adam=False, artifact_alias='latest', batch_size=16, bbox_interval=-1, bucket='', cache_images=False, cfg='cfg/training/yolov7_custom_3.yaml', data='data/custom_data.yaml', device='0', entity=None, epochs=200, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.p5.yaml', image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='yolov7xcustom3', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/yolov7xcustom32', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=16, upload_dataset=False, weights='', workers=8, world_size=1)\n",
            "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\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=0.3, cls_pw=1.0, obj=0.7, 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=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15\n",
            "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1       928  models.common.Conv                      [3, 32, 3, 1]                 \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
            "  5                -2  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
            "  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  9                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 10  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 11                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
            " 12                -1  1         0  models.common.MP                        []                            \n",
            " 13                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 14                -3  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 16          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 18                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 20                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 21                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 22                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 23  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 24                -1  1    263168  models.common.Conv                      [512, 512, 1, 1]              \n",
            " 25                -1  1         0  models.common.MP                        []                            \n",
            " 26                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 27                -3  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 28                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 29          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 30                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 31                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 32                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 33                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 34                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 35                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 36  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 37                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
            " 38                -1  1         0  models.common.MP                        []                            \n",
            " 39                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 40                -3  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 41                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n",
            " 42          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 43                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 44                -2  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 45                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 46                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 47                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 48                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 49  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 50                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
            " 51                -1  1   7609344  models.common.SPPCSPC                   [1024, 512, 1]                \n",
            " 52                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 53                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 54                37  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 55          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 56                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 57                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 58                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
            " 59                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 60                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 61                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 62[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 63                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 64                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 65                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 66                24  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
            " 67          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 68                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 69                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 70                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]               \n",
            " 71                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 72                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 73                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 74[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 75                -1  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
            " 76                -1  1         0  models.common.MP                        []                            \n",
            " 77                -1  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
            " 78                -3  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
            " 79                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 80      [-1, -3, 63]  1         0  models.common.Concat                    [1]                           \n",
            " 81                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 82                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 83                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
            " 84                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 85                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 86                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 87[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 88                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 89                -1  1         0  models.common.MP                        []                            \n",
            " 90                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
            " 91                -3  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
            " 92                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 93      [-1, -3, 51]  1         0  models.common.Concat                    [1]                           \n",
            " 94                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 95                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 96                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]              \n",
            " 97                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 98                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 99                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            "100[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            "101                -1  1   1049600  models.common.Conv                      [2048, 512, 1, 1]             \n",
            "102                75  1    328704  models.common.RepConv                   [128, 256, 3, 1]              \n",
            "103                88  1   1312768  models.common.RepConv                   [256, 512, 3, 1]              \n",
            "104               101  1   5246976  models.common.RepConv                   [512, 1024, 3, 1]             \n",
            "105   [102, 103, 104]  1     44944  models.yolo.IDetect                     [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]\n",
            "Model Summary: 415 layers, 37207344 parameters, 37207344 gradients\n",
            "\n",
            "Scaled weight_decay = 0.0005\n",
            "Optimizer groups: 95 .bias, 95 conv.weight, 98 other\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning 'customdata/train' images and labels... 1001 found, 0 missing, 16 empty, 0 corrupted: 100% 1001/1001 [05:48<00:00,  2.87it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: customdata/train.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning 'customdata/valid' images and labels... 126 found, 0 missing, 4 empty, 0 corrupted: 100% 126/126 [01:20<00:00,  1.56it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: customdata/valid.cache\n",
            "\n",
            "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 3.30, Best Possible Recall (BPR) = 0.9986\n",
            "Image sizes 640 train, 640 test\n",
            "Using 2 dataloader workers\n",
            "Logging results to runs/train/yolov7xcustom32\n",
            "Starting training for 200 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     0/199     8.03G   0.06885    0.7876   0.02248    0.8789        35       640: 100% 63/63 [02:01<00:00,  1.93s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95:   0% 0/4 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95:  50% 2/4 [00:15<00:15,  7.55s/it]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jRf9qxoB3D2X",
        "outputId": "644ddf73-a973-47e7-caa3-8ec77e236f20"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting PyYAML==5.4.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 24.3 MB/s \n",
            "\u001b[?25hInstalling collected packages: PyYAML\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYAML-5.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "WYdSHA3l5N-k"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}