train_yolov7_object_detection_on_custom_data.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGJspIl90XhV",
        "outputId": "5a8390ec-133f-44a7-915a-815925a11aa7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tue Mar 19 15:14:18 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   46C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nD-uPyQ_2jiN",
        "outputId": "76c67d1e-fca9-4a8d-823c-14eabf2fa611"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'yolov7'...\n",
            "remote: Enumerating objects: 579, done.\u001b[K\n",
            "remote: Total 579 (delta 0), reused 0 (delta 0), pack-reused 579\u001b[K\n",
            "Receiving objects: 100% (579/579), 38.53 MiB | 14.11 MiB/s, done.\n",
            "Resolving deltas: 100% (280/280), done.\n",
            "/content/yolov7/yolov7\n",
            "Branch 'fix/problems_associated_with_the_latest_versions_of_pytorch_and_numpy' set up to track remote branch 'fix/problems_associated_with_the_latest_versions_of_pytorch_and_numpy' from 'origin'.\n",
            "Switched to a new branch 'fix/problems_associated_with_the_latest_versions_of_pytorch_and_numpy'\n",
            "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (1.25.2)\n",
            "Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (4.8.0.76)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 7)) (9.4.0)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 8)) (6.0.1)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 9)) (2.31.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 10)) (1.11.4)\n",
            "Requirement already satisfied: torch!=1.12.0,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 11)) (2.2.1+cu121)\n",
            "Requirement already satisfied: torchvision!=0.13.0,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 12)) (0.17.1+cu121)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 13)) (4.66.2)\n",
            "Requirement already satisfied: protobuf<4.21.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 14)) (3.20.3)\n",
            "Requirement already satisfied: tensorboard>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 17)) (2.15.2)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 21)) (1.5.3)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 22)) (0.13.1)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 34)) (7.34.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 35)) (5.9.5)\n",
            "Requirement already satisfied: thop in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 36)) (0.1.1.post2209072238)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (0.10.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (4.49.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (24.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (2.8.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (2.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (2023.7.22)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (4.10.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (12.4.99)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 17)) (1.4.0)\n",
            "Requirement already satisfied: grpcio>=1.48.2 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 17)) (1.62.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 17)) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 17)) (1.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 17)) (3.6)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 17)) (67.7.2)\n",
            "Requirement already satisfied: six>1.9 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 17)) (1.16.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 17)) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 17)) (3.0.1)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->-r requirements.txt (line 21)) (2023.4)\n",
            "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.10/dist-packages (from ipython->-r requirements.txt (line 34)) (0.19.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython->-r requirements.txt (line 34)) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython->-r requirements.txt (line 34)) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython->-r requirements.txt (line 34)) (5.7.1)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython->-r requirements.txt (line 34)) (3.0.43)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython->-r requirements.txt (line 34)) (2.16.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython->-r requirements.txt (line 34)) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython->-r requirements.txt (line 34)) (0.1.6)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython->-r requirements.txt (line 34)) (4.9.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 17)) (5.3.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 17)) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 17)) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard>=2.4.1->-r requirements.txt (line 17)) (1.4.0)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython->-r requirements.txt (line 34)) (0.8.3)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython->-r requirements.txt (line 34)) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->-r requirements.txt (line 34)) (0.2.13)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard>=2.4.1->-r requirements.txt (line 17)) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.12.0,>=1.7.0->-r requirements.txt (line 11)) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 17)) (0.5.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard>=2.4.1->-r requirements.txt (line 17)) (3.2.2)\n"
          ]
        }
      ],
      "source": [
        "# Download YOLOv7 repository and install requirements\n",
        "\n",
        "# !git clone https://github.com/WongKinYiu/yolov7\n",
        "# %cd yolov7\n",
        "# !pip install -r requirements.txt\n",
        "\n",
        "# current version of YOLOv7 is not compatible with pytorch>1.12.1 and numpy>1.20.1\n",
        "# until the appropriate changes get made to the main repository, we will be using a fork containing the patched code\n",
        "# you can track the progress here: https://github.com/roboflow/notebooks/issues/27\n",
        "!git clone https://github.com/SkalskiP/yolov7.git\n",
        "%cd yolov7\n",
        "!git checkout fix/problems_associated_with_the_latest_versions_of_pytorch_and_numpy\n",
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtJ24mPlyF-S"
      },
      "source": [
        "# Download Correctly Formatted Custom Data\n",
        "\n",
        "Next, we'll download our dataset in the right format. Use the `YOLOv7 PyTorch` export. Note that this model requires YOLO TXT annotations, a custom YAML file, and organized directories. The roboflow export writes this for us and saves it in the correct spot.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovKgrVN8ygdW",
        "outputId": "a84e8de5-d398-40d0-bc96-7207899d5f91"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: roboflow in /usr/local/lib/python3.10/dist-packages (1.1.24)\n",
            "Requirement already satisfied: certifi==2023.7.22 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2023.7.22)\n",
            "Requirement already satisfied: chardet==4.0.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.0.0)\n",
            "Requirement already satisfied: cycler==0.10.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.10.0)\n",
            "Requirement already satisfied: idna==2.10 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.10)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.25.2)\n",
            "Requirement already satisfied: opencv-python-headless==4.8.0.74 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.8.0.74)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (9.4.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n",
            "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.31.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.16.0)\n",
            "Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.0.7)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.66.2)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.1)\n",
            "Requirement already satisfied: requests-toolbelt in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)\n",
            "Requirement already satisfied: python-magic in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.4.27)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.2.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.49.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (24.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (3.1.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.3.2)\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading Dataset Version Zip in number-detection-for-v9-10 to yolov7pytorch:: 100%|██████████| 165698/165698 [00:10<00:00, 16186.11it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to number-detection-for-v9-10 in yolov7pytorch:: 100%|██████████| 6270/6270 [00:01<00:00, 3220.76it/s]\n"
          ]
        }
      ],
      "source": [
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"fuOy83MqlC1LtKZ25J0p\")\n",
        "project = rf.workspace(\"digital-meter-recognisation\").project(\"number-detection-for-v9\")\n",
        "version = project.version(10)\n",
        "dataset = version.download(\"yolov7\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLUxLDyTTCij",
        "outputId": "af24def1-5895-4b00-bec2-218ffaf7e73f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found existing installation: numpy 1.25.2\n",
            "Uninstalling numpy-1.25.2:\n",
            "  Would remove:\n",
            "    /usr/local/bin/f2py\n",
            "    /usr/local/bin/f2py3\n",
            "    /usr/local/bin/f2py3.10\n",
            "    /usr/local/lib/python3.10/dist-packages/numpy-1.25.2.dist-info/*\n",
            "    /usr/local/lib/python3.10/dist-packages/numpy.libs/libgfortran-040039e1.so.5.0.0\n",
            "    /usr/local/lib/python3.10/dist-packages/numpy.libs/libopenblas64_p-r0-5007b62f.3.23.dev.so\n",
            "    /usr/local/lib/python3.10/dist-packages/numpy.libs/libquadmath-96973f99.so.0.0.0\n",
            "    /usr/local/lib/python3.10/dist-packages/numpy/*\n",
            "Proceed (Y/n)? Y\n",
            "  Successfully uninstalled numpy-1.25.2\n"
          ]
        }
      ],
      "source": [
        "!pip uninstall numpy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHfT9gEiBsBd"
      },
      "source": [
        "# Begin Custom Training\n",
        "\n",
        "We're ready to start custom training.\n",
        "\n",
        "NOTE: We will only modify one of the YOLOv7 training defaults in our example: `epochs`. We will adjust from 300 to 100 epochs in our example for speed. If you'd like to change other settings, see details in [our accompanying blog post](https://blog.roboflow.com/yolov7-custom-dataset-training-tutorial/)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUbmy674bhpD",
        "outputId": "1cc2bb44-b213-4292-8a3e-2c5f068d9059"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/yolov7\n",
            "--2024-03-19 15:30:34--  https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt\n",
            "Resolving github.com (github.com)... 20.205.243.166\n",
            "Connecting to github.com (github.com)|20.205.243.166|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/13e046d1-f7f0-43ab-910b-480613181b1f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240319%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240319T153034Z&X-Amz-Expires=300&X-Amz-Signature=05e5fa5d4baeebd7b575970c5de3f013f740e254137b3cb6d6e67d3b53144c7f&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7_training.pt&response-content-type=application%2Foctet-stream [following]\n",
            "--2024-03-19 15:30:34--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/13e046d1-f7f0-43ab-910b-480613181b1f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240319%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240319T153034Z&X-Amz-Expires=300&X-Amz-Signature=05e5fa5d4baeebd7b575970c5de3f013f740e254137b3cb6d6e67d3b53144c7f&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7_training.pt&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 75628875 (72M) [application/octet-stream]\n",
            "Saving to: ‘yolov7_training.pt.3’\n",
            "\n",
            "yolov7_training.pt. 100%[===================>]  72.12M   358MB/s    in 0.2s    \n",
            "\n",
            "2024-03-19 15:30:35 (358 MB/s) - ‘yolov7_training.pt.3’ saved [75628875/75628875]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# download COCO starting checkpoint\n",
        "%cd /content/yolov7\n",
        "!wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iqOPKjr22mL",
        "outputId": "4c5ad8ce-e742-45dc-f268-1bfe92e5335d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/yolov7\n",
            "2024-03-19 15:32:48.727635: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-03-19 15:32:48.727686: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-03-19 15:32:48.729115: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-03-19 15:32:48.736916: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-03-19 15:32:49.860651: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "YOLOR 🚀 b2a7de9 torch 2.2.1+cu121 CUDA:0 (Tesla T4, 15102.0625MB)\n",
            "\n",
            "Namespace(weights='yolov7_training.pt', cfg='', data='/content/yolov7/yolov7/number-detection-for-v9-10/data.yaml', hyp='data/hyp.scratch.p5.yaml', epochs=25, batch_size=18, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='exp', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/exp3', total_batch_size=18)\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, loss_ota=1\n",
            "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n",
            "Overriding model.yaml nc=80 with nc=12\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     93490  models.yolo.IDetect                     [12, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:507: 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:3549.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "Model Summary: 415 layers, 37255890 parameters, 37255890 gradients, 105.3 GFLOPS\n",
            "\n",
            "Transferred 557/566 items from yolov7_training.pt\n",
            "Scaled weight_decay = 0.0005625000000000001\n",
            "Optimizer groups: 95 .bias, 95 conv.weight, 98 other\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning 'number-detection-for-v9-10/train/labels.cache' images and labels... 2916 found, 0 missing, 0 empty, 0 corrupted: 100% 2916/2916 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning 'number-detection-for-v9-10/valid/labels.cache' images and labels... 99 found, 0 missing, 0 empty, 0 corrupted: 100% 99/99 [00:00<?, ?it/s]\n",
            "\n",
            "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.36, Best Possible Recall (BPR) = 0.9999\n",
            "Image sizes 640 train, 640 test\n",
            "Using 2 dataloader workers\n",
            "Logging results to runs/train/exp3\n",
            "Starting training for 25 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      0/24     15.1G   0.07453   0.01984   0.03843    0.1328       160       640: 100% 162/162 [03:22<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:15<00:00,  5.05s/it]\n",
            "                 all          99         650       0.556      0.0381       0.027     0.00889\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      1/24       15G   0.05678   0.01718   0.03602      0.11       244       640: 100% 162/162 [03:04<00:00,  1.14s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.12it/s]\n",
            "                 all          99         650       0.158       0.312      0.0799      0.0255\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      2/24     12.2G   0.05297   0.01605   0.03493    0.1039       174       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.15it/s]\n",
            "                 all          99         650       0.194       0.377       0.167      0.0496\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      3/24     14.8G   0.04799   0.01571   0.03287   0.09657       178       640: 100% 162/162 [03:06<00:00,  1.15s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.23it/s]\n",
            "                 all          99         650       0.204       0.426       0.248      0.0839\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      4/24     14.8G   0.04371   0.01541   0.02926   0.08838       227       640: 100% 162/162 [03:03<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.16it/s]\n",
            "                 all          99         650       0.349        0.46       0.442        0.21\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      5/24     14.8G   0.04125   0.01504   0.02555   0.08183       184       640: 100% 162/162 [03:03<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.33it/s]\n",
            "                 all          99         650        0.43       0.518        0.51       0.221\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      6/24     14.8G   0.04056   0.01444   0.02231   0.07731       243       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.33it/s]\n",
            "                 all          99         650       0.569       0.688       0.672       0.336\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      7/24     14.8G   0.03977   0.01416   0.02014   0.07407       163       640: 100% 162/162 [03:03<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.32it/s]\n",
            "                 all          99         650       0.687       0.664       0.714       0.367\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      8/24     14.8G   0.03837   0.01401   0.01748   0.06986       261       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.27it/s]\n",
            "                 all          99         650       0.764       0.648       0.724       0.355\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      9/24     14.8G   0.03708   0.01338   0.01535   0.06581       254       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.12it/s]\n",
            "                 all          99         650       0.812       0.764       0.809        0.43\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     10/24     14.8G   0.03701   0.01298   0.01396   0.06395       243       640: 100% 162/162 [03:02<00:00,  1.12s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.31it/s]\n",
            "                 all          99         650       0.788       0.809       0.831       0.457\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     11/24     14.8G   0.03593   0.01299   0.01251   0.06143       186       640: 100% 162/162 [03:04<00:00,  1.14s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.24it/s]\n",
            "                 all          99         650       0.879       0.809       0.867       0.471\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     12/24     14.8G   0.03527   0.01271   0.01269   0.06066       290       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.36it/s]\n",
            "                 all          99         650       0.878       0.814       0.872       0.476\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     13/24     14.8G   0.03465   0.01274   0.01162   0.05901       185       640: 100% 162/162 [03:03<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.34it/s]\n",
            "                 all          99         650       0.864       0.837       0.889       0.495\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     14/24     14.8G   0.03431   0.01319   0.01045   0.05795       272       640: 100% 162/162 [03:03<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.15it/s]\n",
            "                 all          99         650       0.897       0.826       0.887         0.5\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     15/24     14.8G   0.03449   0.01244    0.0103   0.05722       201       640: 100% 162/162 [03:00<00:00,  1.12s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.35it/s]\n",
            "                 all          99         650       0.868       0.841       0.872       0.489\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     16/24     14.8G   0.03349   0.01239  0.009949   0.05583       211       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.32it/s]\n",
            "                 all          99         650       0.895       0.854       0.898       0.514\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     17/24     14.8G   0.03311   0.01254  0.009103   0.05475       138       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.37it/s]\n",
            "                 all          99         650       0.912       0.843       0.909       0.527\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     18/24     14.8G   0.03247   0.01291  0.009122    0.0545       192       640: 100% 162/162 [03:03<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.25it/s]\n",
            "                 all          99         650       0.855        0.88       0.893       0.516\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     19/24     14.8G   0.03174   0.01231  0.008792   0.05284       146       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.31it/s]\n",
            "                 all          99         650       0.867       0.862       0.898       0.523\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     20/24     14.8G   0.03192   0.01229  0.008692   0.05291       307       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.34it/s]\n",
            "                 all          99         650       0.863       0.844       0.891       0.522\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     21/24     14.8G   0.03119    0.0127  0.008138   0.05203       173       640: 100% 162/162 [03:03<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.03it/s]\n",
            "                 all          99         650       0.933       0.825       0.894       0.541\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     22/24     14.8G   0.03061   0.01246  0.007694   0.05076       288       640: 100% 162/162 [03:04<00:00,  1.14s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.36it/s]\n",
            "                 all          99         650       0.876       0.872       0.902       0.541\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     23/24     14.8G   0.03057   0.01221  0.007554   0.05033       177       640: 100% 162/162 [03:02<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.36it/s]\n",
            "                 all          99         650       0.912       0.862       0.913       0.547\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     24/24     14.8G   0.03025   0.01218  0.007373   0.04981       296       640: 100% 162/162 [03:03<00:00,  1.13s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 3/3 [00:04<00:00,  1.45s/it]\n",
            "                 all          99         650       0.928       0.876       0.919       0.544\n",
            "                   .          99          24       0.906       0.583       0.699        0.22\n",
            "                   0          99         151       0.944       0.874       0.964       0.578\n",
            "                   1          99          62       0.941       0.952       0.967       0.518\n",
            "                   2          99          73       0.929       0.904       0.964       0.622\n",
            "                   3          99          46       0.946       0.826       0.893       0.569\n",
            "                   4          99          48       0.938        0.95       0.941       0.559\n",
            "                   5          99          57       0.914       0.912       0.961       0.518\n",
            "                   6          99          25       0.884       0.917       0.898       0.559\n",
            "                   7          99          40           1       0.967       0.993       0.602\n",
            "                   8          99          54       0.937       0.889       0.906       0.546\n",
            "                   9          99          48       0.977       0.868       0.937       0.589\n",
            "                 Kwh          99          22       0.819       0.864       0.906       0.651\n",
            "25 epochs completed in 1.355 hours.\n",
            "\n",
            "Optimizer stripped from runs/train/exp3/weights/last.pt, 74.9MB\n",
            "Optimizer stripped from runs/train/exp3/weights/best.pt, 74.9MB\n"
          ]
        }
      ],
      "source": [
        "# run this cell to begin training\n",
        "%cd /content/yolov7\n",
        "!python train.py --batch 18 --epochs 25  --data {dataset.location}/data.yaml --weights 'yolov7_training.pt' --device 0\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0W0MpUaTCJro"
      },
      "source": [
        "# Evaluation\n",
        "\n",
        "We can evaluate the performance of our custom training using the provided evalution script.\n",
        "\n",
        "Note we can adjust the below custom arguments. For details, see [the arguments accepted by detect.py](https://github.com/WongKinYiu/yolov7/blob/main/detect.py#L154)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4cfnLtTCIce",
        "outputId": "154d46c1-2a00-4319-c693-d961588e8470"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Namespace(weights=['runs/train/exp3/weights/best.pt'], source='/content/yolov7/yolov7/number-detection-for-v9-10/test/images', img_size=640, conf_thres=0.1, iou_thres=0.45, device='', view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', exist_ok=False, no_trace=False)\n",
            "YOLOR 🚀 b2a7de9 torch 2.2.1+cu121 CUDA:0 (Tesla T4, 15102.0625MB)\n",
            "\n",
            "Fusing layers... \n",
            "RepConv.fuse_repvgg_block\n",
            "RepConv.fuse_repvgg_block\n",
            "RepConv.fuse_repvgg_block\n",
            "IDetect.fuse\n",
            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:507: 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:3549.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "Model Summary: 314 layers, 36541106 parameters, 6194944 gradients, 103.3 GFLOPS\n",
            " Convert model to Traced-model... \n",
            " traced_script_module saved! \n",
            " model is traced! \n",
            "\n",
            "1 0, 3 2s, 1 3, 1 4, 3 5s, 1 6, 1 7, 1 8, 1 9, Done. (22.5ms) Inference, (561.4ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/000000_png_jpg.rf.082a317d205b285faf64972741d9ddbe.jpg\n",
            "2 0s, 1 3, 1 4, 2 6s, 1 8, 1 Kwh, Done. (22.5ms) Inference, (1.2ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/0013141000_434_3250_jpg.rf.d5a699309951480909b6c3122832925b.jpg\n",
            "1 ., 3 0s, 1 3, 1 8, 1 9, 1 Kwh, Done. (22.3ms) Inference, (1.2ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/119_JPG_jpg.rf.4fe1e08fd3d1af7bfca646db0caa13cf.jpg\n",
            "5 0s, 2 1s, 1 4, 1 6, 2 7s, 1 Kwh, Done. (22.3ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/135_jpeg_jpg.rf.82d74f7e0eef34c3d7826ca638d95269.jpg\n",
            "1 0, 1 1, 1 3, 1 5, 1 8, 1 9, Done. (22.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/13_jpg.rf.02347105c8f76e656539d0a49795c40e.jpg\n",
            "1 ., 2 2s, 1 3, 2 5s, 2 7s, 1 Kwh, Done. (22.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/150_JPG_jpg.rf.7529be525b154cd9680923653bef4fe5.jpg\n",
            "1 0, 4 1s, 2 3s, 1 8, Done. (22.3ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/15_jpg.rf.87bc1d3350fb33157bdc289b49814f0b.jpg\n",
            "1 ., 1 1, 1 3, 1 4, 1 8, 1 9, 1 Kwh, Done. (22.3ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/164_JPG_jpg.rf.bbbc4d3c483d970d036fb07406fca746.jpg\n",
            "1 0, 1 2, 2 5s, 1 6, 1 7, Done. (21.2ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/16_jpg.rf.010f3fca6d3d1ada64934db094d221b8.jpg\n",
            "1 ., 1 0, 1 3, 1 4, 1 8, 1 Kwh, Done. (16.8ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/176_JPG_jpg.rf.48137c3fa99b0c1c71b39b5460fb2353.jpg\n",
            "1 0, 1 2, 1 3, 1 4, 1 5, 1 6, 1 7, 1 8, Done. (16.9ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/18_jpg.rf.1b3b61c6d7b464dfab2f5cf20b5fcb24.jpg\n",
            "1 1, 1 2, 1 3, 2 5s, 1 7, 2 9s, Done. (16.8ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/19_jpg.rf.1382269dabfda1916f614727d298e58a.jpg\n",
            "1 0, 1 2, 1 3, 1 4, 2 5s, 1 7, Done. (16.8ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/19_jpg.rf.1bb124036112fa8dd3b0c708ea2dcc74.jpg\n",
            "5 0s, 1 1, 1 7, Done. (16.9ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/1_jpeg_jpg.rf.fac8911da7576a0ea144d4584fd066bc.jpg\n",
            "1 ., 3 0s, 3 1s, 1 3, 3 7s, 1 9, 1 Kwh, Done. (16.2ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/1bb83ddf-9cf5-4bf2-8d0c-9b0c3e3c1916_jpg.rf.27ffa088926da51df742f80f5f7b976d.jpg\n",
            "1 ., 2 1s, 1 2, 1 3, 1 5, 1 6, 1 7, 1 Kwh, Done. (14.2ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/1dd9439a-9cf7-4cd6-aed7-9f264019edc6_jpg.rf.96a3baf83bd604c6d2e6e979751c0b48.jpg\n",
            "3 0s, 1 2, 1 5, 1 7, 2 8s, Done. (14.2ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221104_003114_jpg.rf.022918da258d678dffab7644eb4e46d0.jpg\n",
            "2 0s, 1 2, 2 8s, 1 9, Done. (14.2ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221104_003115_jpg.rf.34a52b900a95bc5eadb3667fab30772e.jpg\n",
            "2 0s, 2 2s, 1 5, 4 8s, Done. (13.9ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221104_003124_jpg.rf.8cf37549ca6db59c0a477c87b78da1fe.jpg\n",
            "1 0, 1 2, 1 4, 2 5s, 1 7, Done. (13.9ms) Inference, (1.6ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221104_003232_jpg.rf.eafa2ee777aee2b46728e35f91a8e8c8.jpg\n",
            "1 0, 1 2, 1 4, 2 5s, 1 7, Done. (13.9ms) Inference, (1.2ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221104_003232_jpg.rf.ec1607ed11d38217911ec3e595024f94.jpg\n",
            "2 0s, 1 2, 1 4, 2 5s, 1 7, Done. (12.7ms) Inference, (1.2ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221104_003236-0-_jpg.rf.8815b1ae8ed7e33c0a62d06fae54d244.jpg\n",
            "1 0, 2 2s, 1 3, 1 6, 3 8s, 1 9, Done. (12.8ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121258_jpg.rf.9a5556fcf96a87680e9c3bfc3194fe89.jpg\n",
            "1 0, 2 2s, 1 3, 1 6, 3 8s, 1 9, Done. (12.7ms) Inference, (1.5ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121258_jpg.rf.e2b3d062a8a7e7719bccded5f5185dc6.jpg\n",
            "1 0, 1 4, 2 5s, 1 8, 1 9, Done. (12.7ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121307_jpg.rf.9d0a42e39244c22ae980628a1da101a2.jpg\n",
            "2 0s, 1 4, 2 5s, 1 8, 1 9, Done. (12.6ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121309_jpg.rf.088cdefa52efd64524e4d6c3a233a6f8.jpg\n",
            "1 0, 1 3, 1 4, 1 5, 1 6, 1 7, 1 9, Done. (12.6ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121321_jpg.rf.e86b313faf8b242da7df0694051c5317.jpg\n",
            "1 0, 1 1, 1 3, 1 4, 2 7s, Done. (12.6ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121326_jpg.rf.43907ac8f5fd49040b4bc36241417a90.jpg\n",
            "1 0, 1 1, 1 3, 1 4, 2 7s, Done. (11.7ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121328-0-_jpg.rf.64141a9d877b91d47a68129e08f62012.jpg\n",
            "1 0, 1 1, 1 6, 2 7s, 1 8, Done. (11.8ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121435-0-_jpg.rf.05437497946eaa7526b0f0066ff14997.jpg\n",
            "1 0, 2 1s, 1 5, 1 6, 1 8, Done. (11.8ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121441-0-_jpg.rf.b0c98ab1534cf1a129b9be10e4f25a8e.jpg\n",
            "1 0, 2 1s, 2 5s, 1 8, Done. (11.7ms) Inference, (1.3ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121441_jpg.rf.81e96b394f9c41d7e1ef29e5dce08a35.jpg\n",
            "1 0, 1 3, 1 4, 2 7s, 1 8, Done. (11.7ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121449_jpg.rf.3ac26d681e80ecfa02ea40a113dfb2e4.jpg\n",
            "1 0, 3 3s, 1 6, 1 8, 1 9, Done. (11.7ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121454_jpg.rf.f18344bd694bbc2a2c9aa73e5e5e1ae7.jpg\n",
            "1 0, 1 2, 2 4s, 1 7, 1 8, Done. (11.7ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121504_jpg.rf.bf45c8097d69ba295f9fe14b1ab9baba.jpg\n",
            "2 0s, 2 1s, 1 2, 1 4, Done. (11.6ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121557_jpg.rf.530b89f38fad0c19e67969fd0c71c1f8.jpg\n",
            "2 0s, 2 1s, 1 2, 1 4, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121557_jpg.rf.b8596cf1b039d0fd7b13b7d0b4fe734f.jpg\n",
            "2 0s, 2 1s, 1 2, 1 4, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121600_jpg.rf.9e7595499cc373bb3aee02ad2aa082f1.jpg\n",
            "2 0s, 1 3, 2 4s, 1 7, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121608_jpg.rf.270086ee2bc3b9e968767a35f765105a.jpg\n",
            "1 0, 2 1s, 1 2, 1 3, 1 5, 1 7, 2 9s, Done. (11.5ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121713_jpg.rf.226aca1dd047745df9707dfde3c3e8d7.jpg\n",
            "1 0, 1 3, 1 5, 1 6, 1 7, 2 8s, 1 9, Done. (11.6ms) Inference, (1.2ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121715_jpg.rf.f0d573b25eb87c8be938ab7c2b0549b0.jpg\n",
            "1 0, 1 2, 2 3s, 1 6, 1 8, 1 9, Done. (11.5ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121736_jpg.rf.fdfc5db65f546ea7be75343ee76823b6.jpg\n",
            "2 0s, 1 4, 1 5, 2 9s, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121827_jpg.rf.9d4f879cce8fb9d5e318cd8d96a94c46.jpg\n",
            "2 0s, 1 4, 2 5s, 2 9s, Done. (11.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121828_jpg.rf.ec1a82a5374d9d646f024b53bc90a4b7.jpg\n",
            "1 0, 2 1s, 2 2s, 1 5, 1 8, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121834_jpg.rf.0ac26493068e72768af3ad7017bd7a68.jpg\n",
            "3 0s, 2 4s, 1 8, Done. (11.4ms) Inference, (1.5ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121838_jpg.rf.f9ecc1b2de0ad974ba8913eac758bfcc.jpg\n",
            "2 0s, 2 7s, 2 8s, 1 9, Done. (11.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121844_jpg.rf.16ed621c90634acfe0f9986549d1071e.jpg\n",
            "2 0s, 1 1, 2 2s, 1 5, Done. (11.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121851_jpg.rf.106145beeb669d7ad5f5390314f93a7a.jpg\n",
            "3 0s, 1 1, 2 2s, 1 5, Done. (11.5ms) Inference, (1.2ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_121857_jpg.rf.673474d24458ccefd15476dc0b1e5794.jpg\n",
            "1 0, 1 2, 1 3, 2 4s, 1 5, 1 7, 1 9, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_122031_jpg.rf.6f69f8427f1241553fe747cfd4818bf1.jpg\n",
            "1 0, 1 2, 1 4, 1 5, 1 7, 1 9, Done. (11.6ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_122032_jpg.rf.773c6b42526f0643c6ce1061252e8f79.jpg\n",
            "1 0, 1 1, 2 2s, 2 9s, Done. (11.4ms) Inference, (1.2ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_122047_jpg.rf.5cd62e06997709ae7c37bbe78f8426e5.jpg\n",
            "1 0, 1 1, 2 2s, 2 9s, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_122049_jpg.rf.ce0b26e2e37ffb5b7a2356c035918e7a.jpg\n",
            "2 0s, 1 2, 1 5, 1 6, 1 7, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_122126_jpg.rf.66b70872e1ec8aae00ba11ea5646de69.jpg\n",
            "2 0s, 1 2, 1 4, 1 6, 1 8, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/20221118_122135_jpg.rf.41a08759b657a10c63e87b6e698f4681.jpg\n",
            "2 0s, 3 1s, 1 2, Done. (11.4ms) Inference, (1.8ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/24_jpg.rf.aaaf187cccab326ea37c3cb792e23214.jpg\n",
            "1 0, 1 4, 1 5, 3 8s, 1 9, Done. (11.4ms) Inference, (1.3ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/27_jpg.rf.b7a6aa8fe6836ba14eff9df7c2558cf5.jpg\n",
            "2 0s, 1 5, 1 6, 1 7, 2 8s, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/2_jpg.rf.cab1b82c189c8186d803de539d534656.jpg\n",
            "2 0s, 1 2, 1 4, 2 5s, 1 7, Done. (11.3ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/30_jpg.rf.242daf139585601ffae60010a29fc511.jpg\n",
            "2 0s, 1 1, 1 2, 2 4s, 1 7, 1 9, Done. (11.5ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/31_jpg.rf.9ee547ecf0ece3ba026955c5a59c74c2.jpg\n",
            "1 0, 1 1, 1 2, 1 3, 1 4, 1 8, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/35_jpg.rf.9d67922526eb3a3e4e5ce4345b9a6100.jpg\n",
            "2 2s, 3 4s, 1 5, 1 6, 1 7, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/37_jpg.rf.c5e6a85becfad955118e9f482e2da710.jpg\n",
            "1 ., 3 0s, 1 1, 2 2s, 1 4, 2 5s, 1 Kwh, Done. (11.5ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/3bbc4062-63e5-4df7-b570-19e244c013b9_jpg.rf.be702b1094e75851fd7d9677a52ee1c2.jpg\n",
            "1 ., 2 0s, 2 1s, 1 2, 1 5, 1 7, 1 8, 1 Kwh, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/3ea46169-9778-4c51-b1d7-21439ff384d5_jpg.rf.79fac0b9d0258b7bba1853a4e9cb9e5c.jpg\n",
            "2 0s, 2 1s, 1 2, 1 3, 1 9, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/40_jpg.rf.b7e2abe34497ef6c523d1df9bca8ef26.jpg\n",
            "2 1s, 1 2, 1 3, 1 4, 1 5, 2 7s, Done. (11.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/44_jpg.rf.c92e6f12815b5a17fdedca34771ffd32.jpg\n",
            "1 ., 3 0s, 2 2s, 1 4, 2 5s, 1 7, 1 Kwh, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/4697b646-61a5-473c-b2e0-f5d47b27452c_jpg.rf.42214284387e9973a1cd28dbe8a07350.jpg\n",
            "2 0s, 1 2, 1 4, 1 5, 2 6s, Done. (11.5ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/48_jpg.rf.a849fb4268153a63d10af4b00d9526af.jpg\n",
            "2 0s, 2 2s, 1 3, 1 4, 2 6s, 1 7, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/48_jpg.rf.e08733ab0664e326a98b5d3373f85b19.jpg\n",
            "1 0, 1 1, 3 5s, 1 8, 2 9s, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/4_jpg.rf.0c03965954019a410240a4f02924cfb6.jpg\n",
            "1 0, 1 1, 3 5s, 1 8, 2 9s, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/4_jpg.rf.e7152c53e8da8dad0c3e84dc2895c6cd.jpg\n",
            "4 0s, 1 1, 2 2s, 1 4, 1 5, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/4a5759ba-df16-4605-92fc-87eaf299e703_jpg.rf.04bb4ec3adfb489db4136fb23cd169dc.jpg\n",
            "1 1, 1 2, 1 5, 1 9, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/4b81f8a4-2d27-4be0-91c9-5173d85e7aff_jpg.rf.376237025c9ee50fe4a878e9e32021b5.jpg\n",
            "2 0s, 1 1, 1 4, 1 5, 1 7, 1 8, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/50_jpg.rf.748f8ac59050c098e7bc23f5bb9803d6.jpg\n",
            "1 ., 1 1, 2 2s, 1 3, 2 5s, 2 7s, 1 Kwh, Done. (11.3ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/50b0c40c-bd3b-4352-a147-bb41ef41ab3f_jpg.rf.13eff96cc54b49e9d214a138dccbd115.jpg\n",
            "1 0, 1 1, 1 2, 1 3, 1 5, 2 6s, 1 9, Done. (11.5ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/52_jpg.rf.2d2bf8c374587117f05c719a8cced5b4.jpg\n",
            "1 0, 1 4, 1 5, 3 6s, 1 7, 1 8, Done. (11.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/54_jpg.rf.9678efb72e2759db01b433bbc9d2ddf7.jpg\n",
            "1 ., 2 2s, 1 3, 2 5s, 1 8, 1 9, Done. (11.4ms) Inference, (1.6ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/55f32f1a-b5fd-43f2-8e2f-6f8484696a7e_jpg.rf.e543ab7e2d1cd9ef5a6a0f1e55596638.jpg\n",
            "1 0, 1 1, 4 3s, 2 5s, 1 6, 1 9, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/56_jpg.rf.1a2d83d5fa406942c32bbece315153d7.jpg\n",
            "1 0, 3 3s, 1 5, 1 9, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/56_jpg.rf.4c5cca48c822c7a9efc00908409c888d.jpg\n",
            "1 ., 1 1, 1 2, 1 5, 2 7s, 1 9, 1 Kwh, Done. (11.3ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/56e62e55-96c0-4497-8bed-5768c8c2ecc4_jpg.rf.38729d51493a24fb5b8bc01408126f4a.jpg\n",
            "1 ., 1 2, 2 3s, 1 7, 1 8, Done. (11.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/57fcb880-8886-42a2-ae2f-beb658cf375d_jpg.rf.4f3c65efa901e989fdef4c13c32bf354.jpg\n",
            "2 2s, 1 5, 2 6s, 2 9s, Done. (11.5ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/59_jpg.rf.73f1ffa4c690d369f65efe9ffd152446.jpg\n",
            "1 ., 2 0s, 2 2s, 2 5s, 1 9, 1 Kwh, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/5bc5c7a3-1d6a-441d-bac1-7e56aa3d0a5c_jpg.rf.180add52945d5bcf4b49ab6c4730e8be.jpg\n",
            "1 ., 3 0s, 1 3, 1 8, 1 9, 1 Kwh, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/601c4406-465c-4be6-89dc-284efc17868b_jpg.rf.70100ab81b3fb095b4f9d9497cd5f23e.jpg\n",
            "1 3, 2 4s, 3 5s, 2 6s, 1 9, Done. (11.5ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/62_jpg.rf.02a83938eeb3c28b37fe099db57e44a7.jpg\n",
            "1 3, 2 4s, 3 5s, 2 6s, 1 9, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/62_jpg.rf.60be7c8e7f7b85d2387b8571d3277f4f.jpg\n",
            "3 0s, 1 1, 1 4, 2 7s, Done. (11.5ms) Inference, (1.3ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/68_jpg.rf.0524d8cdf6a87750e1555ca5fada2f94.jpg\n",
            "2 0s, 1 2, 1 4, 1 5, 2 6s, 2 7s, 1 9, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/73_jpg.rf.fa34f13bcad3a1c7c0430c1fb613e96a.jpg\n",
            "1 2, 2 3s, 1 4, 3 7s, 1 8, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/75_jpg.rf.847c6759c84af3f7bf6bcc1b04209348.jpg\n",
            "1 0, 1 2, 2 3s, 1 4, 2 7s, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/78_jpg.rf.ff04dca1f9bcdddd25dfa886faa7ae2a.jpg\n",
            "1 1, 1 2, 1 3, 2 4s, 1 6, 2 7s, 1 9, Done. (11.3ms) Inference, (1.7ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/79_jpg.rf.7a4ef46275be52b8476c49fbc3ffb19f.jpg\n",
            "3 0s, 1 2, 2 6s, 1 8, 2 9s, Done. (11.5ms) Inference, (1.2ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/7_jpg.rf.a1bbdb9fd1f226788b2417bfb0da9097.jpg\n",
            "1 ., 2 0s, 2 2s, 1 3, 2 5s, 1 7, 1 9, 1 Kwh, Done. (11.4ms) Inference, (1.4ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/7a8106c2-c054-4145-ac00-d8df593faa89_jpg.rf.3b1f758191746c5bac79331e8cc861d2.jpg\n",
            "1 ., 1 1, 1 3, 1 4, 1 8, 1 9, Done. (11.4ms) Inference, (2.2ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/7bdfa63d-51c7-4256-9e61-e8dfde659b3c_jpg.rf.e2597ecba453456d8a3c2f5577bf3209.jpg\n",
            "1 0, 3 1s, 1 4, 1 5, 1 6, 1 7, 1 8, Done. (11.4ms) Inference, (1.3ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/80_jpg.rf.d81523042da79fddc84437578404e6fb.jpg\n",
            "4 0s, 1 6, 2 9s, Done. (11.4ms) Inference, (1.5ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/81_jpg.rf.695ba3bd613b22459d769ef39fa50dee.jpg\n",
            "4 0s, 1 1, 1 6, 2 9s, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/81_jpg.rf.83ffcad7146b2dd5971ccdc21907a2bd.jpg\n",
            "1 0, 1 1, 1 4, 3 6s, 1 7, 1 8, 1 9, Done. (11.3ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/85_jpg.rf.8e594c1c63c33772b300e12d8aa5fcc6.jpg\n",
            "1 ., 1 1, 2 2s, 2 5s, 1 8, 1 9, 1 Kwh, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/86_JPG_jpg.rf.3b8a2020776bc1d6958865cf76e0651f.jpg\n",
            "2 0s, 2 1s, 1 2, 2 4s, 1 9, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/86_jpg.rf.ff824857e23fb6f6db4ee630c83d27ac.jpg\n",
            "1 ., 1 0, 1 1, 2 2s, 2 5s, 1 8, 1 Kwh, Done. (11.4ms) Inference, (1.3ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/88_JPG_jpg.rf.e8b4944a38c19fc6b4df447d14389a33.jpg\n",
            "1 1, 2 2s, 1 6, 1 7, 2 8s, 2 9s, Done. (11.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/89_jpg.rf.2353285857a818ac0feb1a80cb385f1c.jpg\n",
            "1 1, 2 2s, 1 3, 1 4, 1 5, 1 6, 1 7, Done. (11.3ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/93_jpg.rf.2d5c6229a3eed0dbf5246c5b3a89bc48.jpg\n",
            "3 2s, 1 4, 2 5s, 1 6, 1 7, Done. (11.5ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/94_jpg.rf.21d3702325ba255b3bacc8b533c18f62.jpg\n",
            "1 ., 1 1, 3 4s, 1 7, 1 Kwh, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/a9fb1e16-7d44-4370-bd2b-de6e8a75dce4_jpg.rf.a7fe1d92f61d783d7a11a4929a8cb29f.jpg\n",
            "1 0, 1 1, 2 2s, 1 3, 2 5s, Done. (11.6ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/ab905966-0950-4e42-bb12-c7c542755868_jpg.rf.0be57b057fd94596ba2f481bb4255486.jpg\n",
            "11 0s, 2 1s, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/d13a3a3c-96cd-4bf9-9e91-f0f4190b02b2_jpg.rf.d0d6872ddc270fb2cb2e345cfbc8921e.jpg\n",
            "1 ., 3 0s, 1 1, 1 3, 2 4s, 1 6, 1 Kwh, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/efbc3801-5fee-443d-807f-d996e726ddbe_jpg.rf.7e20915b3c8e6d8b990056f9ac666469.jpg\n",
            "1 ., 3 1s, 1 2, 1 5, 2 7s, 1 9, 1 Kwh, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/f5e570fb-630b-4d9b-a157-91911d96caa8_jpg.rf.6211bcac85a75c8fe7eeeea13c487e28.jpg\n",
            "1 ., 4 0s, 1 3, 1 4, 1 7, 1 Kwh, Done. (11.4ms) Inference, (1.0ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/f627925c-ccae-420e-8b2c-5c8e553cdafc_jpg.rf.bf61188f91132dcd051379fa8d27cf18.jpg\n",
            "1 ., 1 2, 1 4, 1 5, 1 8, 2 9s, 1 Kwh, Done. (11.5ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/fa529844-1e48-4fcf-9968-36836af4e22d_jpg.rf.9f5d9f20adfa9b28f99aa7a8a97b3f9e.jpg\n",
            "1 ., 1 0, 1 1, 1 2, 1 5, 1 8, 1 9, 1 Kwh, Done. (11.4ms) Inference, (1.1ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/fb440353-0699-4923-995e-3fb98adbcfde_jpg.rf.6b205b309fda346dc50234701b1f432f.jpg\n",
            "1 0, 1 1, 1 6, 1 8, Done. (11.6ms) Inference, (1.3ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp3/fef59dfc-037c-432b-bcee-2ba519063d72_jpg.rf.fce54dfc5aefef55b74f8501eb9e0ff1.jpg\n",
            "Done. (4.155s)\n"
          ]
        }
      ],
      "source": [
        "# Run evaluation\n",
        "!python detect.py --weights runs/train/exp3/weights/best.pt --conf 0.1 --source {dataset.location}/test/images\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6AGhNOSSHY4_",
        "outputId": "07d7955c-2d1b-489a-a6e9-52a114a14f99"
      },
      "outputs": [],
      "source": [
        "#display inference on ALL test images\n",
        "\n",
        "import glob\n",
        "from IPython.display import Image, display\n",
        "\n",
        "i = 0\n",
        "limit = 10000 # max images to print\n",
        "for imageName in glob.glob('/content/yolov7/runs/detect/exp3/*.jpg'): #assuming JPG\n",
        "    if i < limit:\n",
        "      display(Image(filename=imageName))\n",
        "      print(\"\\n\")\n",
        "    i = i + 1\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CMOfi7eLJCT3"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMumI7a2JDAN"
      },
      "source": [
        "# Reparameterize for Inference\n",
        "\n",
        "https://github.com/WongKinYiu/yolov7/blob/main/tools/reparameterization.ipynb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S0td_3-S-XeG"
      },
      "source": [
        "## Deploy Model on Roboflow\n",
        "\n",
        "Once you have finished training your YOLOv7 model, you’ll have a set of trained weights ready for use. These weights will be in the `/content/runs/train/exp/weights/best.pt` folder of your project. You can upload your model weights to Roboflow Deploy to use your trained weights on our infinitely scalable infrastructure.\n",
        "\n",
        "The `.deploy()` function in the [Roboflow pip package](https://docs.roboflow.com/python) supports uploading YOLOv7 weights.\n",
        "\n",
        "**Before you run this code, make sure you create a new Version in the Roboflow dashboard following the instructions we covered earlier. Fill in your project name, workspace, and version number below.**\n",
        "\n",
        "[Learn how to retrieve your project ID](https://docs.roboflow.com/api-reference/workspace-and-project-ids)\n",
        "[Learn how to retrieve your API key.](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "boPUd9Wx-XeG"
      },
      "outputs": [],
      "source": [
        "rf = Roboflow(api_key=\"YOUR_API_KEY\")\n",
        "project = rf.workspace(\"workspace\").project(\"project\")\n",
        "dataset = project.version(1)\n",
        "\n",
        "project.version(dataset.version).deploy(model_type=\"yolov7\", model_path=f\"/content/runs/train/exp/weights/\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9XmuyXM-XeG"
      },
      "source": [
        "# Deploy Your Model to the Edge\n",
        "\n",
        "In addition to using the Roboflow hosted API for deployment, you can use [Roboflow Inference](https://inference.roboflow.com), an open source inference solution that has powered millions of API calls in production environments. Inference works with CPU and GPU, giving you immediate access to a range of devices, from the NVIDIA Jetson to TRT-compatible devices to ARM CPU devices.\n",
        "\n",
        "With Roboflow Inference, you can self-host and deploy your model on-device. You can deploy applications using the [Inference Docker containers](https://inference.roboflow.com/quickstart/docker/) or the pip package.\n",
        "\n",
        "For example, to install Inference on a device with an NVIDIA GPU, we can use:\n",
        "\n",
        "```\n",
        "docker pull roboflow/roboflow-inference-server-gpu\n",
        "```\n",
        "\n",
        "Then we can run inference via HTTP:\n",
        "\n",
        "```python\n",
        "import requests\n",
        "\n",
        "workspace_id = \"\"\n",
        "model_id = \"\"\n",
        "image_url = \"\"\n",
        "confidence = 0.75\n",
        "api_key = \"\"\n",
        "iou_threshold = 0.5\n",
        "\n",
        "infer_payload = {\n",
        "    \"image\": {\n",
        "        \"type\": \"url\",\n",
        "        \"value\": image_url,\n",
        "    },\n",
        "    \"confidence\": confidence,\n",
        "    \"iou_threshold\": iou_threshold,\n",
        "    \"api_key\": api_key,\n",
        "}\n",
        "res = requests.post(\n",
        "    f\"http://localhost:9001/{workspace_id}/{model_id}\",\n",
        "    json=infer_object_detection_payload,\n",
        ")\n",
        "\n",
        "predictions = res.json()\n",
        "```\n",
        "\n",
        "Above, set your Roboflow workspace ID, model ID, and API key.\n",
        "\n",
        "- [Find your workspace and model ID](https://docs.roboflow.com/api-reference/workspace-and-project-ids?ref=blog.roboflow.com)\n",
        "- [Find your API key](https://docs.roboflow.com/api-reference/authentication?ref=blog.roboflow.com#retrieve-an-api-key)\n",
        "\n",
        "Also, set the URL of an image on which you want to run inference. This can be a local file.\n",
        "\n",
        "_To use your YOLOv5 model commercially with Inference, you will need a Roboflow Enterprise license, through which you gain a pass-through license for using YOLOv5. An enterprise license also grants you access to features like advanced device management, multi-model containers, auto-batch inference, and more._"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "JFa3BFJQszMC",
        "outputId": "f73adca1-a44a-4647-9962-d09badc62dc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'content' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-1b8bc43d7ca0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcontent\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0myolov7\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mruns\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mexp3\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtxt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'content' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "pd.read_csv(\"/content/yolov7/runs/train/exp3/results.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "swnGg8d1s11R",
        "outputId": "e6c245f8-8ea6-46ab-d522-fec9fd13fb9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         0/24     15.1G   0.07453   0.01984   0.03843    0.1328       160       640    0.5565   0.03808   0.02702  0.008893    0.0861   0.02661   0.03906\n",
              "0         1/24       15G   0.05678   0.01718   0.0...                                                                                                    \n",
              "1         2/24     12.2G   0.05297   0.01605   0.0...                                                                                                    \n",
              "2         3/24     14.8G   0.04799   0.01571   0.0...                                                                                                    \n",
              "3         4/24     14.8G   0.04371   0.01541   0.0...                                                                                                    \n",
              "4         5/24     14.8G   0.04125   0.01504   0.0...                                                                                                    \n",
              "5         6/24     14.8G   0.04056   0.01444   0.0...                                                                                                    \n",
              "6         7/24     14.8G   0.03977   0.01416   0.0...                                                                                                    \n",
              "7         8/24     14.8G   0.03837   0.01401   0.0...                                                                                                    \n",
              "8         9/24     14.8G   0.03708   0.01338   0.0...                                                                                                    \n",
              "9        10/24     14.8G   0.03701   0.01298   0.0...                                                                                                    \n",
              "10       11/24     14.8G   0.03593   0.01299   0.0...                                                                                                    \n",
              "11       12/24     14.8G   0.03527   0.01271   0.0...                                                                                                    \n",
              "12       13/24     14.8G   0.03465   0.01274   0.0...                                                                                                    \n",
              "13       14/24     14.8G   0.03431   0.01319   0.0...                                                                                                    \n",
              "14       15/24     14.8G   0.03449   0.01244    0....                                                                                                    \n",
              "15       16/24     14.8G   0.03349   0.01239  0.00...                                                                                                    \n",
              "16       17/24     14.8G   0.03311   0.01254  0.00...                                                                                                    \n",
              "17       18/24     14.8G   0.03247   0.01291  0.00...                                                                                                    \n",
              "18       19/24     14.8G   0.03174   0.01231  0.00...                                                                                                    \n",
              "19       20/24     14.8G   0.03192   0.01229  0.00...                                                                                                    \n",
              "20       21/24     14.8G   0.03119    0.0127  0.00...                                                                                                    \n",
              "21       22/24     14.8G   0.03061   0.01246  0.00...                                                                                                    \n",
              "22       23/24     14.8G   0.03057   0.01221  0.00...                                                                                                    \n",
              "23       24/24     14.8G   0.03025   0.01218  0.00...                                                                                                    "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2533d312-772e-4f3f-9b4e-657010731761\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0/24     15.1G   0.07453   0.01984   0.03843    0.1328       160       640    0.5565   0.03808   0.02702  0.008893    0.0861   0.02661   0.03906</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1/24       15G   0.05678   0.01718   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2/24     12.2G   0.05297   0.01605   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3/24     14.8G   0.04799   0.01571   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4/24     14.8G   0.04371   0.01541   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5/24     14.8G   0.04125   0.01504   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6/24     14.8G   0.04056   0.01444   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7/24     14.8G   0.03977   0.01416   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8/24     14.8G   0.03837   0.01401   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9/24     14.8G   0.03708   0.01338   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10/24     14.8G   0.03701   0.01298   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11/24     14.8G   0.03593   0.01299   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12/24     14.8G   0.03527   0.01271   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13/24     14.8G   0.03465   0.01274   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14/24     14.8G   0.03431   0.01319   0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15/24     14.8G   0.03449   0.01244    0....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16/24     14.8G   0.03349   0.01239  0.00...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17/24     14.8G   0.03311   0.01254  0.00...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18/24     14.8G   0.03247   0.01291  0.00...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19/24     14.8G   0.03174   0.01231  0.00...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20/24     14.8G   0.03192   0.01229  0.00...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21/24     14.8G   0.03119    0.0127  0.00...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22/24     14.8G   0.03061   0.01246  0.00...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23/24     14.8G   0.03057   0.01221  0.00...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24/24     14.8G   0.03025   0.01218  0.00...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2533d312-772e-4f3f-9b4e-657010731761')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2533d312-772e-4f3f-9b4e-657010731761 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2533d312-772e-4f3f-9b4e-657010731761');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4f4ae2a8-8361-4bc4-bc4b-3f7bbb0e2379\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4f4ae2a8-8361-4bc4-bc4b-3f7bbb0e2379')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4f4ae2a8-8361-4bc4-bc4b-3f7bbb0e2379 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 24,\n  \"fields\": [\n    {\n      \"column\": \"      0/24     15.1G   0.07453   0.01984   0.03843    0.1328       160       640    0.5565   0.03808   0.02702  0.008893    0.0861   0.02661   0.03906\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"      9/24     14.8G   0.03708   0.01338   0.01535   0.06581       254       640    0.8122    0.7644    0.8092    0.4297   0.05012    0.0194   0.02056\",\n          \"     17/24     14.8G   0.03311   0.01254  0.009103   0.05475       138       640    0.9118    0.8433    0.9093    0.5266   0.03851    0.0217   0.01233\",\n          \"      1/24       15G   0.05678   0.01718   0.03602      0.11       244       640    0.1578    0.3122    0.0799   0.02548   0.07933     0.022   0.03744\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cd"
      ],
      "metadata": {
        "id": "AIaVfzaHt_M7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SdZ9dI-gtsCw"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}