fire_smoke_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To download dataset to train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ESQzMFbLy-xO",
    "outputId": "2fc89276-99ab-49ef-8036-73ae445c0e6a"
   },
   "outputs": [],
   "source": [
    "from roboflow import Roboflow\n",
    "# rf = Roboflow(api_key=\"eTmaFD48qf2SCqyzs9lE\")\n",
    "# project = rf.workspace(\"middle-east-tech-university\").project(\"fire-and-smoke-detection-hiwia\")\n",
    "# version = project.version(2)\n",
    "# dataset = version.download(\"yolov11\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics import settings\n",
    "\n",
    "# Update a setting\n",
    "settings.update({\"datasets_dir\": \"fire-and-smoke-detection-2\"})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the model for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U8Q2pPdcyQyO",
    "outputId": "cca6072e-92bb-466d-da01-c21b24de3291"
   },
   "outputs": [],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "# # Load a pretrained model\n",
    "model = YOLO(\"model_weights\\\\yolo11n.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L0WqjzO_3jbc",
    "outputId": "0285b0c6-77b2-44d1-fb55-a816a9da2d16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!yolo task=detect mode=train  model='model_weights\\\\yolo11n.pt' data=\"fire-and-smoke-detection-2/data.yaml\" epochs=50 imgsz=640 plots=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Thunder\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 197, in _run_module_as_main\n",
      "    return _run_code(code, main_globals, None,\n",
      "  File \"C:\\Users\\Thunder\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 87, in _run_code\n",
      "    exec(code, run_globals)\n",
      "  File \"c:\\Users\\Thunder\\PycharmProjects\\personal_projects\\fire_smoke_detection\\.venv\\Scripts\\yolo.exe\\__main__.py\", line 7, in <module>\n",
      "  File \"c:\\users\\thunder\\pycharmprojects\\personal_projects\\fire_smoke_detection\\.venv\\lib\\site-packages\\ultralytics\\cfg\\__init__.py\", line 905, in entrypoint\n",
      "    raise ValueError(f\"Invalid 'mode={mode}'. Valid modes are {MODES}.\\n{CLI_HELP_MSG}\")\n",
      "ValueError: Invalid 'mode=test'. Valid modes are {'track', 'val', 'train', 'export', 'benchmark', 'predict'}.\n",
      "\n",
      "    Arguments received: ['yolo', 'task=detect', 'mode=test', \"model='model_weights\\\\\\\\yolo11n-pose.pt'\", 'data=fire-and-smoke-detection-2/data.yaml', 'epochs=50', 'imgsz=640', 'plots=True']. Ultralytics 'yolo' commands use the following syntax:\n",
      "\n",
      "        yolo TASK MODE ARGS\n",
      "\n",
      "        Where   TASK (optional) is one of {'obb', 'pose', 'segment', 'detect', 'classify'}\n",
      "                MODE (required) is one of {'track', 'val', 'train', 'export', 'benchmark', 'predict'}\n",
      "                ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.\n",
      "                    See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'\n",
      "\n",
      "    1. Train a detection model for 10 epochs with an initial learning_rate of 0.01\n",
      "        yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01\n",
      "\n",
      "    2. Predict a YouTube video using a pretrained segmentation model at image size 320:\n",
      "        yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320\n",
      "\n",
      "    3. Val a pretrained detection model at batch-size 1 and image size 640:\n",
      "        yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640\n",
      "\n",
      "    4. Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required)\n",
      "        yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128\n",
      "\n",
      "    5. Streamlit real-time webcam inference GUI\n",
      "        yolo streamlit-predict\n",
      "\n",
      "    6. Ultralytics solutions usage\n",
      "        yolo solutions count or in ['count', 'heatmap', 'queue', 'speed', 'workout', 'analytics', 'help'] source=\"path/to/video/file.mp4\"\n",
      "\n",
      "    7. Run special commands:\n",
      "        yolo help\n",
      "        yolo checks\n",
      "        yolo version\n",
      "        yolo settings\n",
      "        yolo copy-cfg\n",
      "        yolo cfg\n",
      "        yolo solutions help\n",
      "\n",
      "    Docs: https://docs.ultralytics.com\n",
      "    Solutions: https://docs.ultralytics.com/solutions/\n",
      "    Community: https://community.ultralytics.com\n",
      "    GitHub: https://github.com/ultralytics/ultralytics\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "\n",
    "!yolo task=detect mode=val model='model_weights\\\\yolo11n-pose.pt' data=\"fire-and-smoke-detection-2/data.yaml\" epochs=50 imgsz=640 plots=True"
   ]
  }
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