GunDetectionAlgorithm.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NptwqKcsUFsr"
      },
      "outputs": [],
      "source": [
        "# connect to google drive\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# change directory\n",
        "%cd /content/gdrive/MyDrive"
      ],
      "metadata": {
        "id": "cFh0iXk5U4n6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create file it's not exist \n",
        "import os \n",
        "if not os.path.isdir(\"VisionIsAllYouNeed\"):\n",
        "  os.makedirs(\"VisionIsAllYouNeed\")"
      ],
      "metadata": {
        "id": "SbEZuaWNVn7K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# change directory to the created directory \n",
        "%cd /content/gdrive/MyDrive/VisionIsAllYouNeed"
      ],
      "metadata": {
        "id": "Gl_ZBI7vWAKw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check the current directory \n",
        "!pwd"
      ],
      "metadata": {
        "id": "1znoNymxXTrh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/WongKinYiu/yolov7"
      ],
      "metadata": {
        "id": "kOtOW66CXdIP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt"
      ],
      "metadata": {
        "id": "jrpfuWSJYjbX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"
      ],
      "metadata": {
        "id": "8tZ7HYzKY0vE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd yolov7 "
      ],
      "metadata": {
        "id": "KcrZGZ_TY8Eo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python train.py --device 0 --batch-size 16 --epochs 100 --img 640 640 --data data/custom_data.yaml --hyp data/hyp.scratch.custom.yaml --cfg cfg/training/yolov7-custom.yaml --weight yolov7.pt --name yolov7-custom  "
      ],
      "metadata": {
        "id": "Cz-y7OkMdDXm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python detect.py --weight /content/gdrive/MyDrive/VisionIsAllYouNeed/yolov7/runs/train/yolov7-custom/weights/best.pt --conf 0.4 --img-size 640 --source data/testSamples/12.png"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQ6Cm0eB3XXY",
        "outputId": "b7e16019-b8e8-48f9-d943-ce9e726e88ec"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', no_trace=False, nosave=False, project='runs/detect', save_conf=False, save_txt=False, source='data/testSamples/12.png', update=False, view_img=False, weights=['/content/gdrive/MyDrive/VisionIsAllYouNeed/yolov7/runs/train/yolov7-custom/weights/best.pt'])\n",
            "YOLOR 🚀 v0.1-121-g2fdc7f1 torch 1.13.0+cu116 CUDA:0 (Tesla T4, 15109.75MB)\n",
            "\n",
            "Fusing layers... \n",
            "RepConv.fuse_repvgg_block\n",
            "RepConv.fuse_repvgg_block\n",
            "RepConv.fuse_repvgg_block\n",
            "IDetect.fuse\n",
            "Model Summary: 314 layers, 36481772 parameters, 6194944 gradients\n",
            " Convert model to Traced-model... \n",
            " traced_script_module saved! \n",
            " model is traced! \n",
            "\n",
            "/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: 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:3190.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "1 jack sparrow, Done. (18.3ms) Inference, (1.5ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp13/12.png\n",
            "Done. (1.108s)\n"
          ]
        }
      ]
    }
  ]
}