leaf_detection_YOLOv7x.ipynb

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  "metadata": {
    "colab": {
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      "gpuType": "T4"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<h1  align=center><font  size = 5>Leaf Detection using YOLOv7x</font></h1>\n",
        "\n",
        "<img  src=\"https://i0.wp.com/www.bigtoolbox.com/wp-content/uploads/2019/09/weeds-in-a-vegtable-garden.jpg?resize=1080%2C675&ssl=1\"  height=500  width=1000  alt=\"https://www.bigtoolbox.com/weeds-in-your-vegetable-garden/\">  \n",
        "\n",
        "<small>Picture Source: <a  href=\"https://www.bigtoolbox.com/weeds-in-your-vegetable-garden/\">bigtoolbox</a></small>  \n",
        "\n",
        "<br>\n",
        "\n",
        "## Summary\n",
        "\n",
        "This project utilizes the YOLOv7 architecture to develop an accurate and efficient leaf object detection model for agricultural applications. By leveraging a curated dataset of annotated images, the model can identify and classify leaves, aiding in crop management, disease detection, and weed control. The project focuses on robustness, efficiency, and interpretability, aiming to empower farmers and researchers with a reliable tool for informed decision-making. Through optimization and evaluation, the model demonstrates its potential to enhance crop productivity and sustainability.\n",
        "\n",
        "<br>\n",
        "\n",
        "## Introduction\n",
        "\n",
        "Detecting and classifying objects within images is a fundamental challenge in the field of computer vision, and it finds numerous practical applications across various domains. In particular, accurate and efficient detection of specific objects, such as leaves, is crucial in agricultural settings for tasks like crop management, disease identification, and weed control.\n",
        "\n",
        "<br>\n",
        "\n",
        "In this project, we present a leaf object detection model based on the state-of-the-art **You Only Look Once** version 7 (YOLOv7) architecture. *YOLOv7* is a deep learning framework that achieves real-time object detection with impressive accuracy. By harnessing the power of convolutional neural networks and advanced computer vision techniques, our model aims to provide an effective and scalable solution for leaf detection in complex agricultural environments. To train and evaluate our *YOLOv7* leaf object detection model, we leverage a comprehensive dataset specifically curated for this purpose. The dataset contains diverse images of crops and weeds, annotated with bounding boxes that precisely delineate leaf objects. This rich dataset empowers our model to learn intricate patterns and generalizable representations, enabling it to accurately detect and classify leaves under varying conditions.\n",
        "\n",
        "<br>\n",
        "\n",
        "Throughout the development of our leaf object detection model, we adhere to the principles of robustness, efficiency, and interpretability. We optimize our model's architecture and hyperparameters to strike a balance between accuracy and computational efficiency, ensuring real-time performance in practical scenarios. Additionally, we focus on interpretable outputs, providing insights into the detected leaf objects and their corresponding bounding boxes.\n",
        "\n",
        "<br>\n",
        "\n",
        "Our ultimate goal is to empower farmers, researchers, and agricultural professionals with a reliable tool for leaf object detection that can facilitate informed decision-making and enhance crop management practices. By accurately identifying and analyzing leaves, we enable precise monitoring, early detection of diseases, and targeted interventions, ultimately leading to improved yields and sustainable agricultural practices.\n",
        "\n",
        "<br>\n",
        "\n",
        "## YOLO\n",
        "\n",
        "*YOLO (You Only Look Once)* is an object detection algorithm that was introduced in 2015 by **Joseph Redmon** et al. It revolutionized the field of computer vision by providing a real-time object detection solution with impressive accuracy.\n",
        "\n",
        "\n",
        "\n",
        "*   **Single Pass Detection**: YOLO takes a different approach compared to traditional object detection methods that use region proposal techniques. Instead of dividing the image into regions and examining each region separately, YOLO performs detection in a single pass. It divides the input image into a grid and predicts bounding boxes and class probabilities for each grid cell.\n",
        "\n",
        "*   **Grid-based Prediction**: YOLO divides the input image into a fixed-size grid, typically, say, 7x7 or 13x13. Each grid cell is responsible for predicting objects that fall within it. For each grid cell, YOLO predicts multiple bounding boxes (each associated with a confidence score) and class probabilities.\n",
        "\n",
        "*   **Anchor Boxes**: To handle objects of different sizes and aspect ratios, YOLO uses anchor boxes. These anchor boxes are pre-defined boxes of different shapes and sizes. Each anchor box is associated with a specific grid cell. The network predicts offsets and dimensions for anchor boxes relative to the grid cell, along with the confidence scores and class probabilities.\n",
        "\n",
        "*   **Training**: YOLO is trained using a combination of labeled bounding box annotations and classification labels. The training process involves optimizing the network to minimize the localization loss (related to the accuracy of bounding box predictions) and the classification loss (related to the accuracy of class predictions).\n",
        "\n",
        "*   **Speed and Accuracy Trade-off**: YOLO achieves real-time object detection by sacrificing some localization accuracy compared to slower methods like Faster R-CNN. However, it still achieves competitive accuracy while providing significantly faster inference speeds, making it well-suited for real-time applications.\n",
        "\n",
        "<br>\n",
        "\n",
        "Since its introduction, YOLO has undergone several improvements and variations. Different versions such as YOLOv2, YOLOv3, and YOLOv4 have been developed, each introducing enhancements in terms of accuracy, speed, and additional features.\n",
        "\n",
        "It's important to note that this is a high-level overview of YOLO, and the algorithm has many technical details and variations. For a more in-depth understanding, it's recommended to refer to the original YOLO papers and related resources.\n",
        "\n",
        "<br>\n",
        "\n",
        "## Keywords  \n",
        "\n",
        "<ul>\n",
        " <li>YOLOv7</li>\n",
        " <li>Leaf object detection</li>\n",
        " <li>Computer vision</li>\n",
        " <li>Agricultural applications</li>\n",
        " <li>Deep learning</li>\n",
        "</ul>\n",
        "\n",
        "<br>\n",
        "\n",
        "<h1>Objective for this Notebook</h1>\n",
        "\n",
        "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
        "  <ol>\n",
        "      <li><a href=\"\">Importing Libraries</a></li>\n",
        "      <li><a href=\"\">Data Preprocessing</a></li>  \n",
        "      <li><a href=\"\">Clone YOLOv7 and Train the Model</a></li>\n",
        "      <li><a href=\"\">Uplad Test Images and Make Predictions</a></li>\n",
        "      <li><a href=\"\">Save the Results</a></li>\n",
        "  </ol>\n",
        "</div>\n",
        "<br>\n",
        "\n",
        "Feel free to modify and customize this introduction to align with your specific project objectives and goals. Good luck with your *YOLOv7* leaf object detection model!\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "q_LtQGXNQA6t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make sure your runtime is **GPU** (_not_ CPU or TPU). And if it is an option, make sure you are using _Python 3_. You can select these settings by going to `Runtime -> Change runtime type -> Select the above mentioned settings and then press SAVE`."
      ],
      "metadata": {
        "id": "ZPZgEqROj8tB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Libraries"
      ],
      "metadata": {
        "id": "Nut7NattQ-C6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import random\n",
        "import shutil"
      ],
      "metadata": {
        "id": "qFRLkWriPzV0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ],
      "metadata": {
        "id": "1z5v8sjlSrh0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset\n",
        "The dataset used in this project is the Crop and Weed Detection Data with Bounding Boxes from Kaggle, which can be accessed [here](https://www.kaggle.com/datasets/ravirajsinh45/crop-and-weed-detection-data-with-bounding-boxes).\n",
        "\n",
        "<br>\n",
        "\n",
        "###  Description\n",
        "[The Crop and Weed Detection dataset](https://www.kaggle.com/datasets/ravirajsinh45/crop-and-weed-detection-data-with-bounding-boxes) is a comprehensive collection of images specifically curated for leaf object detection in agricultural environments. It consists of a diverse range of images showcasing crops and weeds in various growth stages and conditions.\n",
        "\n",
        "Each image in the dataset is annotated with bounding boxes that precisely delineate the leaf objects, providing ground truth information for training and evaluation. The annotations enable the model to learn intricate patterns and accurately identify and classify leaves within the images.\n",
        "\n",
        "<br>\n",
        "\n",
        "### Contents\n",
        "The dataset includes the following components:\n",
        "\n",
        "1. Images: A collection of high-resolution images depicting crops and weeds in different agricultural settings.\n",
        "\n",
        "2. Annotations: XML files containing the bounding box coordinates and class labels for each leaf object present in the corresponding image.\n",
        "\n",
        "<br>\n",
        "\n",
        "### Preprocessing\n",
        "As part of the data preprocessing pipeline, the images were resized and standardized to a consistent resolution suitable for training the YOLOv7 leaf object detection model. The annotations were parsed and converted into a compatible format for training and evaluation purposes.\n",
        "\n",
        "<br>\n",
        "\n",
        "### Usage\n",
        "To utilize the Crop and Weed Detection dataset in this project, please follow these steps:\n",
        "\n",
        "1. Download the dataset from the provided Kaggle link.\n",
        "\n",
        "2. Extract the dataset files to a local directory.\n",
        "\n",
        "3. Ensure that the dataset is properly organized, with images and corresponding annotation files placed in the appropriate folders.\n",
        "\n",
        "4. Follow the installation and setup instructions in the project's README file to integrate the dataset into the training and evaluation pipeline."
      ],
      "metadata": {
        "id": "U0TvJa9wmu8p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwMyw7FRY8v4",
        "outputId": "067e014b-af42-4d33-fc5d-935d4b6529db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -q /content/archive.zip"
      ],
      "metadata": {
        "id": "5vbraRsGP7_b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm -rf /content/agri_data\n",
        "# !rm -rf /content/classes.txt\n",
        "# !rm -rf /content/archive.zip"
      ],
      "metadata": {
        "id": "jpbeiWc3VTxW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "folder_path = \"/content/agri_data/data\""
      ],
      "metadata": {
        "id": "01fZoL-3P0f5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create directories for train and val."
      ],
      "metadata": {
        "id": "ze7NdGYsYwIQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir = os.path.join(folder_path, \"train\")\n",
        "val_dir = os.path.join(folder_path, \"val\")\n",
        "os.makedirs(train_dir, exist_ok=True)\n",
        "os.makedirs(val_dir, exist_ok=True)"
      ],
      "metadata": {
        "id": "OSqnHw5EP1lX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create images and labels directories within train and val folders."
      ],
      "metadata": {
        "id": "e8ESfYBbYulH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_images_dir = os.path.join(train_dir, \"images\")\n",
        "train_labels_dir = os.path.join(train_dir, \"labels\")\n",
        "val_images_dir = os.path.join(val_dir, \"images\")\n",
        "val_labels_dir = os.path.join(val_dir, \"labels\")\n",
        "os.makedirs(train_images_dir, exist_ok=True)\n",
        "os.makedirs(train_labels_dir, exist_ok=True)\n",
        "os.makedirs(val_images_dir, exist_ok=True)\n",
        "os.makedirs(val_labels_dir, exist_ok=True)"
      ],
      "metadata": {
        "id": "o4-6JNtYYtbL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get a list of all image files in the folder."
      ],
      "metadata": {
        "id": "OJ4xVwsHYx4N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image_files = [f for f in os.listdir(folder_path) if f.endswith(\".jpeg\")]"
      ],
      "metadata": {
        "id": "atltv4VKP21Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_val_files = int(len(image_files) * 0.2)\n",
        "val_files = random.sample(image_files, num_val_files)"
      ],
      "metadata": {
        "id": "SCEZ0O2LP4Ns"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for val_file in val_files:\n",
        "    image_file = os.path.join(folder_path, val_file)\n",
        "    annotation_file = os.path.join(folder_path, val_file.replace(\".jpeg\", \".txt\"))\n",
        "    try:\n",
        "        shutil.move(image_file, val_images_dir)\n",
        "        shutil.move(annotation_file, val_labels_dir)\n",
        "    except FileNotFoundError:\n",
        "        print(f\"File not found: {val_file}\")\n",
        "\n",
        "for image_file in image_files:\n",
        "    annotation_file = os.path.join(folder_path, image_file.replace(\".jpeg\", \".txt\"))\n",
        "    try:\n",
        "        shutil.move(os.path.join(folder_path, image_file), train_images_dir)\n",
        "        shutil.move(annotation_file, train_labels_dir)\n",
        "    except FileNotFoundError:\n",
        "        print(f\"File not found: {image_file}\")\n",
        "\n",
        "print(\"Data splitting completed!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THED2AuFP6b3",
        "outputId": "1b57ed22-b3b2-44d8-a494-98d4f1ccd1d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Data splitting completed!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_images_dir = \"/content/agri_data/data/train/images\"\n",
        "val_images_dir = \"/content/agri_data/data/val/images\"\n",
        "\n",
        "train_image_count = len([f for f in os.listdir(train_images_dir) if f.endswith(\".jpeg\")])\n",
        "val_image_count = len([f for f in os.listdir(val_images_dir) if f.endswith(\".jpeg\")])\n",
        "\n",
        "print(f\"Number of images in train folder: {train_image_count}\")\n",
        "print(f\"Number of images in val folder: {val_image_count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tRpMs_fQW59X",
        "outputId": "83f77ab6-3d83-4872-f87c-9a9b7a14de76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images in train folder: 1040\n",
            "Number of images in val folder: 260\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels_dir = \"/content/agri_data/data/train/labels\"\n",
        "val_labels_dir = \"/content/agri_data/data/val/labels\"\n",
        "\n",
        "train_txt_count = len([f for f in os.listdir(train_labels_dir) if f.endswith(\".txt\")])\n",
        "val_txt_count = len([f for f in os.listdir(val_labels_dir) if f.endswith(\".txt\")])\n",
        "\n",
        "print(f\"Number of TXT files in train labels folder: {train_txt_count}\")\n",
        "print(f\"Number of TXT files in val labels folder: {val_txt_count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_KL7oxtZ-xv",
        "outputId": "d33c3eaa-7bb2-402c-f102-d116be0beb66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of TXT files in train labels folder: 1040\n",
            "Number of TXT files in val labels folder: 260\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clone YOLOv7 and Train the Model"
      ],
      "metadata": {
        "id": "_OsRjWzAiMK4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyeY9H6ccVMQ",
        "outputId": "80fe4337-eb54-4677-a1f6-6d3229ccb464"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/WongKinYiu/yolov7.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6bdPZ3JRaQN",
        "outputId": "50732f29-d9e4-40d5-d42b-bcd3ebdc956b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov7'...\n",
            "remote: Enumerating objects: 1157, done.\u001b[K\n",
            "remote: Counting objects: 100% (18/18), done.\u001b[K\n",
            "remote: Compressing objects: 100% (10/10), done.\u001b[K\n",
            "remote: Total 1157 (delta 10), reused 15 (delta 8), pack-reused 1139\u001b[K\n",
            "Receiving objects: 100% (1157/1157), 70.42 MiB | 34.79 MiB/s, done.\n",
            "Resolving deltas: 100% (496/496), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/yolov7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiTXUokERr-Q",
        "outputId": "fa4c8065-0669-4e4e-b736-8c9dc65dae67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can change model from [WongKinYiu GitHub](https://github.com/WongKinYiu/yolov7) page."
      ],
      "metadata": {
        "id": "SlmH3y-vSHF-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dl2K_DqaRwnB",
        "outputId": "60bac9e7-a63a-44c1-fc18-0f564003c07a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-21 11:09:19--  https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/c0e9f375-a42b-45d5-9e96-3156476cf121?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230521%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230521T110919Z&X-Amz-Expires=300&X-Amz-Signature=a1272363c387bd048e66492a7ac7a157634dfd3d77855896930dd2f4ea716fba&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7x.pt&response-content-type=application%2Foctet-stream [following]\n",
            "--2023-05-21 11:09:19--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/511187726/c0e9f375-a42b-45d5-9e96-3156476cf121?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230521%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230521T110919Z&X-Amz-Expires=300&X-Amz-Signature=a1272363c387bd048e66492a7ac7a157634dfd3d77855896930dd2f4ea716fba&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=511187726&response-content-disposition=attachment%3B%20filename%3Dyolov7x.pt&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 143099649 (136M) [application/octet-stream]\n",
            "Saving to: ‘yolov7x.pt’\n",
            "\n",
            "yolov7x.pt          100%[===================>] 136.47M   162MB/s    in 0.8s    \n",
            "\n",
            "2023-05-21 11:09:20 (162 MB/s) - ‘yolov7x.pt’ saved [143099649/143099649]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before training, you neet to go to `yolov7/data/coco.yaml` and define your number of class, class names and train-val paths like that:\n",
        "\n",
        "```\n",
        "# COCO 2017 dataset http://cocodataset.org\n",
        "\n",
        "train: ../agri_data/data/train/images\n",
        "val: ../agri_data/data/val/images\n",
        "\n",
        "# number of classes\n",
        "nc: 2\n",
        "\n",
        "# class names\n",
        "names: ['crop', 'weed']\n",
        "```"
      ],
      "metadata": {
        "id": "Qzy2B3ueTCf7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   `!python train.py`: This is the command to execute the Python script *train.py* for training the YOLOv7 model.\n",
        "\n",
        "*  `--device 0`: This parameter specifies the device (GPU) to be used for training. In this case, it is set to device 0, indicating the first GPU device.\n",
        "\n",
        "*  `--batch-size 16`: This parameter determines the number of images in each batch during training. A batch size of *16* means that the model will process 16 images at a time before updating the weights.\n",
        "\n",
        "*  `--data data/coco.yaml`: This parameter specifies the path to the YAML file containing the dataset configuration. In this case, **the *coco.yaml* file is used, which provides information about the dataset, including the classes and paths to the training and validation data.**\n",
        "\n",
        "*  `--img 640 640`: This parameter sets the input image size for the model. The YOLOv7 model requires square input images, and here the dimensions are set to 640x640 pixels.\n",
        "\n",
        "*  `--epochs 64`: This parameter defines the number of epochs, which represents the number of times the entire training dataset will be passed through the model during training. In this case, the model will be trained for **64 epochs**.\n",
        "\n",
        "*  `--weights yolov7x.pt`: This parameter specifies the initial weights of the model. The *yolov7x.pt* file contains the pre-trained weights for the *YOLOv7* model, which will be used as the starting point for training.\n",
        "\n",
        "*  `--hyp data/hyp.scratch.p5.yaml`: This parameter indicates the path to the YAML file containing hyperparameters for training. Hyperparameters include learning rate, weight decay, and other settings that affect the training process. Here, the *hyp.scratch.p5.yaml* file is used.\n",
        "\n",
        "*  `--name yolov7x`: This parameter sets the name of the model during training. The name can be customized, and in this case, it is set to *yolov7x*.\n",
        "\n"
      ],
      "metadata": {
        "id": "RPr-LxU9UJCG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you are using GPU, try this:"
      ],
      "metadata": {
        "id": "Z_9PdmaMP5cw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python train.py --device 0 --batch-size 16 --data data/coco.yaml --img 640 640 --epochs 64 --weights yolov7x.pt --hyp data/hyp.scratch.p5.yaml --name yolov7x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yrN77YHAcMpV",
        "outputId": "e0ef0884-71c4-437f-c6c2-4782a9f5beba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-05-21 11:09:57.292501: 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 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-05-21 11:09:58.216048: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "YOLOR 🚀 v0.1-122-g3b41c2c torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n",
            "\n",
            "Namespace(weights='yolov7x.pt', cfg='', data='data/coco.yaml', hyp='data/hyp.scratch.p5.yaml', epochs=64, batch_size=16, 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='yolov7x', 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/yolov7x', total_batch_size=16)\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=2\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      1160  models.common.Conv                      [3, 40, 3, 1]                 \n",
            "  1                -1  1     28960  models.common.Conv                      [40, 80, 3, 2]                \n",
            "  2                -1  1     57760  models.common.Conv                      [80, 80, 3, 1]                \n",
            "  3                -1  1    115520  models.common.Conv                      [80, 160, 3, 2]               \n",
            "  4                -1  1     10368  models.common.Conv                      [160, 64, 1, 1]               \n",
            "  5                -2  1     10368  models.common.Conv                      [160, 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  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 11                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 12[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]                           \n",
            " 13                -1  1    103040  models.common.Conv                      [320, 320, 1, 1]              \n",
            " 14                -1  1         0  models.common.MP                        []                            \n",
            " 15                -1  1     51520  models.common.Conv                      [320, 160, 1, 1]              \n",
            " 16                -3  1     51520  models.common.Conv                      [320, 160, 1, 1]              \n",
            " 17                -1  1    230720  models.common.Conv                      [160, 160, 3, 2]              \n",
            " 18          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 19                -1  1     41216  models.common.Conv                      [320, 128, 1, 1]              \n",
            " 20                -2  1     41216  models.common.Conv                      [320, 128, 1, 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  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 24                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 25                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 26                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 27[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]                           \n",
            " 28                -1  1    410880  models.common.Conv                      [640, 640, 1, 1]              \n",
            " 29                -1  1         0  models.common.MP                        []                            \n",
            " 30                -1  1    205440  models.common.Conv                      [640, 320, 1, 1]              \n",
            " 31                -3  1    205440  models.common.Conv                      [640, 320, 1, 1]              \n",
            " 32                -1  1    922240  models.common.Conv                      [320, 320, 3, 2]              \n",
            " 33          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 34                -1  1    164352  models.common.Conv                      [640, 256, 1, 1]              \n",
            " 35                -2  1    164352  models.common.Conv                      [640, 256, 1, 1]              \n",
            " 36                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 37                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 38                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 39                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 40                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 41                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 42[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]                           \n",
            " 43                -1  1   1640960  models.common.Conv                      [1280, 1280, 1, 1]            \n",
            " 44                -1  1         0  models.common.MP                        []                            \n",
            " 45                -1  1    820480  models.common.Conv                      [1280, 640, 1, 1]             \n",
            " 46                -3  1    820480  models.common.Conv                      [1280, 640, 1, 1]             \n",
            " 47                -1  1   3687680  models.common.Conv                      [640, 640, 3, 2]              \n",
            " 48          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 49                -1  1    328192  models.common.Conv                      [1280, 256, 1, 1]             \n",
            " 50                -2  1    328192  models.common.Conv                      [1280, 256, 1, 1]             \n",
            " 51                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 52                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 53                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 54                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 55                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 56                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 57[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]                           \n",
            " 58                -1  1   1640960  models.common.Conv                      [1280, 1280, 1, 1]            \n",
            " 59                -1  1  11887360  models.common.SPPCSPC                   [1280, 640, 1]                \n",
            " 60                -1  1    205440  models.common.Conv                      [640, 320, 1, 1]              \n",
            " 61                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 62                43  1    410240  models.common.Conv                      [1280, 320, 1, 1]             \n",
            " 63          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 64                -1  1    164352  models.common.Conv                      [640, 256, 1, 1]              \n",
            " 65                -2  1    164352  models.common.Conv                      [640, 256, 1, 1]              \n",
            " 66                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 67                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 68                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 69                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 70                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 71                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 72[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]                           \n",
            " 73                -1  1    410240  models.common.Conv                      [1280, 320, 1, 1]             \n",
            " 74                -1  1     51520  models.common.Conv                      [320, 160, 1, 1]              \n",
            " 75                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 76                28  1    102720  models.common.Conv                      [640, 160, 1, 1]              \n",
            " 77          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 78                -1  1     41216  models.common.Conv                      [320, 128, 1, 1]              \n",
            " 79                -2  1     41216  models.common.Conv                      [320, 128, 1, 1]              \n",
            " 80                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 81                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 82                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 83                -1  1    147712  models.common.Conv                      [128, 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, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]                           \n",
            " 87                -1  1    102720  models.common.Conv                      [640, 160, 1, 1]              \n",
            " 88                -1  1         0  models.common.MP                        []                            \n",
            " 89                -1  1     25920  models.common.Conv                      [160, 160, 1, 1]              \n",
            " 90                -3  1     25920  models.common.Conv                      [160, 160, 1, 1]              \n",
            " 91                -1  1    230720  models.common.Conv                      [160, 160, 3, 2]              \n",
            " 92      [-1, -3, 73]  1         0  models.common.Concat                    [1]                           \n",
            " 93                -1  1    164352  models.common.Conv                      [640, 256, 1, 1]              \n",
            " 94                -2  1    164352  models.common.Conv                      [640, 256, 1, 1]              \n",
            " 95                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 96                -1  1    590336  models.common.Conv                      [256, 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  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            "101[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]                           \n",
            "102                -1  1    410240  models.common.Conv                      [1280, 320, 1, 1]             \n",
            "103                -1  1         0  models.common.MP                        []                            \n",
            "104                -1  1    103040  models.common.Conv                      [320, 320, 1, 1]              \n",
            "105                -3  1    103040  models.common.Conv                      [320, 320, 1, 1]              \n",
            "106                -1  1    922240  models.common.Conv                      [320, 320, 3, 2]              \n",
            "107      [-1, -3, 59]  1         0  models.common.Concat                    [1]                           \n",
            "108                -1  1    656384  models.common.Conv                      [1280, 512, 1, 1]             \n",
            "109                -2  1    656384  models.common.Conv                      [1280, 512, 1, 1]             \n",
            "110                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n",
            "111                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n",
            "112                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n",
            "113                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n",
            "114                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n",
            "115                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]              \n",
            "116[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]                           \n",
            "117                -1  1   1639680  models.common.Conv                      [2560, 640, 1, 1]             \n",
            "118                87  1    461440  models.common.Conv                      [160, 320, 3, 1]              \n",
            "119               102  1   1844480  models.common.Conv                      [320, 640, 3, 1]              \n",
            "120               117  1   7375360  models.common.Conv                      [640, 1280, 3, 1]             \n",
            "121   [118, 119, 120]  1     47103  models.yolo.Detect                      [2, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [320, 640, 1280]]\n",
            "Model Summary: 459 layers, 70819527 parameters, 70819527 gradients\n",
            "\n",
            "Transferred 632/638 items from yolov7x.pt\n",
            "Scaled weight_decay = 0.0005\n",
            "Optimizer groups: 108 .bias, 108 conv.weight, 105 other\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../agri_data/data/train/labels' images and labels... 1040 found, 0 missing, 0 empty, 0 corrupted: 100% 1040/1040 [00:00<00:00, 3518.49it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../agri_data/data/train/labels.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../agri_data/data/val/labels' images and labels... 260 found, 0 missing, 0 empty, 0 corrupted: 100% 260/260 [00:00<00:00, 2496.36it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../agri_data/data/val/labels.cache\n",
            "\n",
            "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 3.80, Best Possible Recall (BPR) = 1.0000\n",
            "Image sizes 640 train, 640 test\n",
            "Using 2 dataloader workers\n",
            "Logging results to runs/train/yolov7x\n",
            "Starting training for 64 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      0/63     14.3G   0.06553   0.01874    0.0166    0.1009        69       640: 100% 65/65 [01:38<00:00,  1.52s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95:   0% 0/9 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:18<00:00,  2.01s/it]\n",
            "                 all         260         421       0.145       0.233       0.105      0.0274\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      1/63     14.3G   0.05346   0.01691   0.01448   0.08484        41       640: 100% 65/65 [01:21<00:00,  1.26s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:07<00:00,  1.22it/s]\n",
            "                 all         260         421       0.328       0.389       0.296       0.118\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      2/63     14.3G   0.05178   0.01461   0.01196   0.07835        45       640: 100% 65/65 [01:21<00:00,  1.26s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.476       0.394       0.366       0.137\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      3/63     14.3G   0.05771   0.01246   0.01053    0.0807        61       640: 100% 65/65 [01:21<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.563       0.486       0.429       0.186\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      4/63     14.3G   0.05154   0.01239  0.009906   0.07384        65       640: 100% 65/65 [01:21<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:07<00:00,  1.27it/s]\n",
            "                 all         260         421       0.791       0.553       0.653       0.344\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      5/63     14.1G   0.04875   0.01191   0.00902   0.06969        52       640: 100% 65/65 [01:21<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421        0.82       0.591       0.742       0.385\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      6/63     14.1G   0.04996   0.01157   0.00812   0.06965        39       640: 100% 65/65 [01:21<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.723       0.709       0.715       0.357\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      7/63     14.1G   0.04497   0.01123  0.007395   0.06359        47       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.784       0.686       0.766       0.427\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      8/63     14.1G   0.05036   0.01063  0.008204    0.0692        62       640: 100% 65/65 [01:21<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.625       0.577       0.628       0.317\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "      9/63     14.1G   0.05023   0.01052  0.007999   0.06875        57       640: 100% 65/65 [01:21<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.829        0.72       0.813       0.449\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     10/63     14.1G   0.04887   0.01035  0.007121   0.06635        83       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.676       0.713       0.684        0.33\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     11/63     14.1G   0.05004   0.01085  0.007661   0.06855        48       640: 100% 65/65 [01:21<00:00,  1.26s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.29it/s]\n",
            "                 all         260         421       0.498       0.654       0.502       0.239\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     12/63     14.1G   0.04656   0.01037  0.007448   0.06438        64       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.654        0.56       0.612        0.33\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     13/63     14.1G   0.04572   0.01022   0.00681   0.06275        62       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.709       0.745        0.77       0.403\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     14/63     14.1G   0.04485   0.01057  0.006918   0.06233        47       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421        0.67        0.64       0.659        0.37\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     15/63     14.1G   0.04675   0.01029  0.007622   0.06467        61       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.714       0.591        0.66       0.369\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     16/63     14.1G   0.04871   0.01042  0.006712   0.06584        51       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.285       0.458       0.301       0.138\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     17/63     14.1G   0.04699   0.01031   0.00711   0.06442        62       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.254       0.423       0.269       0.132\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     18/63     14.1G   0.04833   0.01044  0.007703   0.06647        52       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.651        0.59       0.626       0.348\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     19/63     14.1G   0.04624   0.01047  0.007533   0.06424        50       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.239        0.34        0.16      0.0716\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     20/63     14.1G   0.04466   0.01012  0.006485   0.06126        52       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.34it/s]\n",
            "                 all         260         421       0.641       0.662       0.641        0.36\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     21/63     14.1G   0.04333    0.0101  0.006783   0.06022        58       640: 100% 65/65 [01:20<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.378       0.487       0.378       0.183\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     22/63     14.1G   0.04504   0.01071   0.00897   0.06472        71       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.697       0.673       0.723       0.415\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     23/63     14.1G   0.04358   0.01065  0.007266    0.0615        53       640: 100% 65/65 [01:21<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.34it/s]\n",
            "                 all         260         421       0.542       0.555        0.51       0.274\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     24/63     14.1G   0.04617   0.01036  0.007901   0.06443        50       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.681       0.701       0.737       0.424\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     25/63     14.1G   0.04282  0.009919  0.006423   0.05916        50       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.35it/s]\n",
            "                 all         260         421       0.731       0.775       0.791       0.476\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     26/63     14.1G   0.03954   0.00996  0.005906    0.0554        62       640: 100% 65/65 [01:20<00:00,  1.23s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421        0.72       0.753       0.766       0.465\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     27/63     14.1G   0.04098   0.01005  0.006593   0.05762        71       640: 100% 65/65 [01:19<00:00,  1.23s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.712       0.752       0.776       0.474\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     28/63     14.1G   0.04024   0.01049   0.00636   0.05709        85       640: 100% 65/65 [01:20<00:00,  1.23s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.668       0.759       0.724       0.441\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     29/63     14.1G   0.03994   0.01025  0.006412    0.0566        74       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.35it/s]\n",
            "                 all         260         421       0.771       0.771       0.818       0.514\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     30/63     14.1G   0.03975    0.0103  0.006449    0.0565        75       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.746       0.758         0.8       0.491\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     31/63     14.1G   0.03828   0.01006   0.00648   0.05482        53       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.655       0.782       0.768       0.482\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     32/63     14.1G    0.0385  0.009775    0.0063   0.05457        66       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.605       0.758        0.69        0.41\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     33/63     14.1G   0.03742   0.01026  0.006396   0.05408        54       640: 100% 65/65 [01:20<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.728       0.747       0.768       0.461\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     34/63     14.1G   0.03959  0.009896  0.006457   0.05595        43       640: 100% 65/65 [01:21<00:00,  1.25s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.712       0.757       0.761       0.466\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     35/63     14.1G   0.03672  0.009743  0.006465   0.05293        79       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.731       0.731       0.775       0.478\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     36/63     14.1G   0.03709   0.01006  0.006028   0.05317        69       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.793       0.751       0.826        0.52\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     37/63     14.1G   0.03821  0.009609  0.005136   0.05295        67       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.737       0.726       0.791       0.498\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     38/63     14.1G   0.03487   0.01011  0.005334   0.05031        80       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.754       0.748       0.809       0.524\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     39/63     14.1G   0.03535   0.01011  0.005705   0.05117        63       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.666       0.768       0.778       0.489\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     40/63     14.1G   0.03606   0.01034  0.005753   0.05215        67       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.34it/s]\n",
            "                 all         260         421       0.816        0.67       0.775       0.493\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     41/63     14.1G   0.03492   0.01042  0.006131   0.05147        64       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.681       0.772       0.765        0.48\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     42/63     14.1G   0.03478   0.01012  0.005666   0.05057        51       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.711       0.738       0.787       0.497\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     43/63     14.1G   0.03566   0.01047  0.005321   0.05145        97       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.824       0.689        0.82       0.507\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     44/63     14.1G   0.03523   0.01023  0.005496   0.05096        52       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.754       0.796        0.83       0.522\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     45/63     14.1G   0.03485   0.01011  0.004869   0.04983        68       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.734       0.742       0.813       0.511\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     46/63     14.1G   0.03571   0.01018  0.005472   0.05136        72       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.806       0.769       0.859       0.547\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     47/63     14.1G   0.03433  0.009788  0.005062   0.04918        49       640: 100% 65/65 [01:19<00:00,  1.23s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.772       0.706       0.823       0.522\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     48/63     14.1G   0.03351  0.009809  0.004699   0.04802        47       640: 100% 65/65 [01:19<00:00,  1.23s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.764        0.73       0.825       0.526\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     49/63     14.1G   0.03388  0.009717  0.004061   0.04766        42       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.767       0.781        0.85       0.547\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     50/63     14.1G   0.03386  0.009835  0.005172   0.04886        45       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.724       0.819       0.837       0.543\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     51/63     14.1G   0.03257  0.009374  0.004625   0.04657        54       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.736       0.796       0.844        0.55\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     52/63     14.1G   0.03358  0.009985  0.004112   0.04768        48       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.831       0.735       0.851       0.552\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     53/63     14.1G   0.03317  0.009908  0.003766   0.04685        64       640: 100% 65/65 [01:20<00:00,  1.23s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421        0.76       0.513       0.623       0.419\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     54/63     14.1G   0.03253  0.009862  0.004196   0.04659        86       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.745       0.828       0.864       0.567\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     55/63     14.1G   0.03304  0.009797  0.003805   0.04665       109       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421       0.767       0.755       0.838       0.537\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     56/63     14.1G   0.03462  0.009808  0.004333   0.04876        82       640: 100% 65/65 [01:20<00:00,  1.24s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.787       0.763       0.844       0.549\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     57/63     14.1G   0.03227  0.009681  0.004011   0.04596        72       640: 100% 65/65 [01:19<00:00,  1.23s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.30it/s]\n",
            "                 all         260         421        0.74       0.781       0.826       0.547\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     58/63     14.1G   0.03203  0.009883  0.004106   0.04602        58       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.31it/s]\n",
            "                 all         260         421       0.795       0.758        0.85       0.558\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     59/63     14.1G   0.03155   0.01001  0.003856   0.04542        66       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.32it/s]\n",
            "                 all         260         421       0.767        0.78       0.845       0.548\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     60/63     14.1G   0.03342  0.009911  0.004807   0.04814        68       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.34it/s]\n",
            "                 all         260         421       0.769       0.789       0.859       0.557\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     61/63     14.1G    0.0306  0.009603  0.003535   0.04373        43       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421        0.75       0.787       0.848       0.553\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     62/63     14.1G   0.03049  0.009359  0.003197   0.04304        97       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:06<00:00,  1.33it/s]\n",
            "                 all         260         421       0.802       0.763       0.856        0.56\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
            "     63/63     14.1G   0.02969  0.009495   0.00381   0.04299        64       640: 100% 65/65 [01:19<00:00,  1.22s/it]\n",
            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 9/9 [00:08<00:00,  1.06it/s]\n",
            "                 all         260         421       0.834       0.724       0.855       0.561\n",
            "                crop         260         264       0.768       0.754       0.848       0.551\n",
            "                weed         260         157         0.9       0.694       0.861       0.571\n",
            "64 epochs completed in 1.725 hours.\n",
            "\n",
            "Optimizer stripped from runs/train/yolov7x/weights/last.pt, 142.1MB\n",
            "Optimizer stripped from runs/train/yolov7x/weights/best.pt, 142.1MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you are using CPU, try this:"
      ],
      "metadata": {
        "id": "0CE0CWiXP8ex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !python train.py --device cpu --batch-size 16 --data data/coco.yaml --img 640 640 --epochs 64 --weights yolov7x.pt --hyp data/hyp.scratch.p5.yaml --name yolov7x"
      ],
      "metadata": {
        "id": "Mw5WsRIzP_pU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Uplad Test Images and Make Predictions"
      ],
      "metadata": {
        "id": "09ytXwsAiGbN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/"
      ],
      "metadata": {
        "id": "7dJQLwK9hlYq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d45accce-fd6c-431c-fbb2-c41b0bf006af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir test"
      ],
      "metadata": {
        "id": "wzI4IJDyhplR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/test"
      ],
      "metadata": {
        "id": "JaSlMix3h61W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bf704ee-d815-49c7-c274-6d89acf451fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/test\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "id": "Fv43JlOwh94U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "711d747c-da85-4a27-cd76-46eda3534b96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5174d58b-1429-48ca-8640-7eca38e3a69e\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5174d58b-1429-48ca-8640-7eca38e3a69e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving agri_0_14.jpeg to agri_0_14.jpeg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/yolov7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GB_TlOL3YqAU",
        "outputId": "394f19f7-0d3f-4da0-b5e8-5f68d854bd05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Upload your images into test folder."
      ],
      "metadata": {
        "id": "K11lx1fshuUk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python detect.py --weights runs/train/yolov7x/weights/best.pt --conf 0.50 --img-size 640 --source /content/test/agri_0_14.jpeg"
      ],
      "metadata": {
        "id": "wV6OQ1qXhRiK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20258b8e-cc04-429f-99ed-b7d1cf60b13d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Namespace(weights=['runs/train/yolov7x/weights/best.pt'], source='/content/test/agri_0_14.jpeg', img_size=640, conf_thres=0.5, 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 🚀 v0.1-122-g3b41c2c torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 354 layers, 70786879 parameters, 0 gradients\n",
            " Convert model to Traced-model... \n",
            " traced_script_module saved! \n",
            " model is traced! \n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "10 crops, Done. (36.3ms) Inference, (1.7ms) NMS\n",
            " The image with the result is saved in: runs/detect/exp/agri_0_14.jpeg\n",
            "Done. (0.077s)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In addition, you can upload video and make predictions."
      ],
      "metadata": {
        "id": "NsplmcWYiuFe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source test/video_name.mp4"
      ],
      "metadata": {
        "id": "CAK0wYFHi0w0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save the Results"
      ],
      "metadata": {
        "id": "BQ6sxcSCE2U0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "files.download('runs/train/yolov7x/weights/best.pt')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/F1_curve.png')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/PR_curve.png')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/confusion_matrix.png')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/hyp.yaml')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/opt.yaml')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/results.png')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/results.txt')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/test_batch0_labels.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/test_batch0_pred.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/test_batch1_labels.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/test_batch1_pred.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/test_batch2_labels.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/test_batch2_pred.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch0.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch1.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch2.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch3.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch4.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch5.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch6.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch7.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch8.jpg')\n",
        "files.download('/content/yolov7/runs/train/yolov7x/train_batch9.jpg')\n",
        "files.download('/content/yolov7/runs/detect/exp/agri_0_14.jpeg')\n",
        "files.download('/content/yolov7/data/coco.yaml')"
      ],
      "metadata": {
        "id": "kOvj_lCGE5Xs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "cca7f084-a9b2-4150-814d-89ff808ac6c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_99c5e4af-a94e-441d-b9cc-7f722e679a34\", \"best.pt\", 142106919)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_5baa5b87-af2d-4921-92ae-7821a500b33d\", \"F1_curve.png\", 140423)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_b72a4749-1fd2-4602-a71f-c02bfb19a002\", \"PR_curve.png\", 112466)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_8a2afcea-0739-41a3-ac84-ea6f862f3741\", \"confusion_matrix.png\", 92405)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "download(\"download_8f0b61a3-7089-417b-bf0c-29dadf587726\", \"hyp.yaml\", 400)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "download(\"download_a202d144-5b46-408e-b43a-950b2d4afa63\", \"opt.yaml\", 670)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "download(\"download_c0283385-8c7e-42dc-8ea2-c3d40612e699\", \"results.png\", 276622)"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_1ffabfad-fb7c-44b8-bb04-ccdaed5a730d\", \"results.txt\", 9664)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_dd1aa964-4f9f-4d78-9fdf-c495aad5725a\", \"test_batch0_labels.jpg\", 415433)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_ca62ba57-49b7-4bda-975b-2df7a7e190b7\", \"test_batch0_pred.jpg\", 419177)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_7fb3b976-c204-4ee6-b663-85e61982730d\", \"test_batch1_labels.jpg\", 448713)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_1fae5a44-8173-4a8c-80bb-ed866fd54528\", \"test_batch1_pred.jpg\", 453868)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "download(\"download_4fe00a36-e4db-4298-8ac7-d223d2c0a897\", \"test_batch2_labels.jpg\", 426470)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_79ca095f-697f-4751-b553-dacd0832fd24\", \"test_batch2_pred.jpg\", 434925)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
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              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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            "application/javascript": [
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
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              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
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              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
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              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
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              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
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              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
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              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
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              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
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              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
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              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
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              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
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              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
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              "  "
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              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
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              "      const div = document.createElement('div');\n",
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              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
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              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
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              "      div.remove();\n",
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              "  "
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            "application/javascript": [
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
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              "      label.textContent = `Downloading \"${filename}\": `;\n",
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              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
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              "            progress.value = downloaded;\n",
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              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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            "application/javascript": [
              "download(\"download_6e142b15-db0a-4c02-9ba7-f35af5c9a07d\", \"train_batch5.jpg\", 367233)"
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          "metadata": {}
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          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
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              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
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              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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            "application/javascript": [
              "download(\"download_4cb52b26-eefc-4e8f-af9b-663061b41961\", \"train_batch6.jpg\", 351567)"
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          "data": {
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
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              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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          "data": {
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            "application/javascript": [
              "download(\"download_19abbc45-1791-4455-baa9-f64cd8cb0744\", \"train_batch7.jpg\", 355022)"
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          "data": {
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              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
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              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
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              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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            "application/javascript": [
              "download(\"download_4f529a41-022a-4e41-964c-093757aedf43\", \"train_batch8.jpg\", 286646)"
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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          "output_type": "display_data",
          "data": {
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            "application/javascript": [
              "download(\"download_c24aec99-8662-4adb-b605-c06ece49f1c1\", \"train_batch9.jpg\", 366954)"
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          "metadata": {}
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          "data": {
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
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              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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          "output_type": "display_data",
          "data": {
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            "application/javascript": [
              "download(\"download_c5d25fc9-f048-4887-b8d9-369fa0c5aded\", \"agri_0_14.jpeg\", 159806)"
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>Contact Me</h1>\n",
        "<p>If you have something to say to me please contact me:</p>\n",
        "\n",
        "<ul>\n",
        "  <li>Twitter: <a href=\"https://twitter.com/Doguilmak\">Doguilmak</a></li>\n",
        "  <li>Mail address: doguilmak@gmail.com</li>\n",
        "</ul>"
      ],
      "metadata": {
        "id": "-1-U5fDNFlI_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime\n",
        "print(f\"Changes have been made to the project on {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")"
      ],
      "metadata": {
        "id": "QZfvhOQKFlmq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fead351-3811-4c43-b3f8-0439dbf341c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Changes have been made to the project on 2023-05-21 13:11:22\n"
          ]
        }
      ]
    }
  ]
}