{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "HOME = os.getcwd()\n",
    "print(HOME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pip install method (recommended)\n",
    "\n",
    "%pip install ultralytics==8.0.20\n",
    "\n",
    "from IPython import display\n",
    "display.clear_output()\n",
    "\n",
    "import ultralytics\n",
    "ultralytics.checks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "from IPython.display import display, Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir {HOME}/datasets\n",
    "%cd {HOME}/datasets\n",
    "\n",
    "%pip install roboflow --quiet\n",
    "\n",
    "from roboflow import Roboflow\n",
    "rf = Roboflow(api_key=\"YOUR_API_KEY\")\n",
    "project = rf.workspace(\"WORKSPACE\").project(\"PROJECT\")\n",
    "dataset = project.version(1).download(\"yolov8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd {HOME}\n",
    "\n",
    "!yolo task=detect mode=train model=yolov8s.pt data={dataset.location}/data.yaml epochs=25 imgsz=800 plots=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd {HOME}\n",
    "Image(filename=f'{HOME}/runs/detect/train/confusion_matrix.png', width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd {HOME}\n",
    "Image(filename=f'{HOME}/runs/detect/train/results.png', width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd {HOME}\n",
    "Image(filename=f'{HOME}/runs/detect/train/val_batch0_pred.jpg', width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd {HOME}\n",
    "\n",
    "!yolo task=detect mode=val model={HOME}/runs/detect/train/weights/best.pt data={dataset.location}/data.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd {HOME}\n",
    "!yolo task=detect mode=predict model={HOME}/runs/detect/train/weights/best.pt conf=0.25 source={dataset.location}/test/images save=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "from IPython.display import Image, display\n",
    "\n",
    "for image_path in glob.glob(f'{HOME}/runs/detect/predict3/*.jpg')[:3]:\n",
    "      display(Image(filename=image_path, width=600))\n",
    "      print(\"\\n\")"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}