{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a37220a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, World!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello, World!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d318d1f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Van: 1111 images\n",
      "Taxi: 748 images\n",
      "Bicycle: 1618 images\n",
      "Bus: 2133 images\n",
      "Car: 6781 images\n",
      "Motorcycle: 2986 images\n",
      "Truck: 2033 images\n",
      "NonVehicles: 8968 images\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "data_dir = '../data/raw/vehicle_classification'\n",
    "\n",
    "for class_name in os.listdir(data_dir):\n",
    "    class_path = os.path.join(data_dir, class_name)\n",
    "    if os.path.isdir(class_path):\n",
    "        count = len(os.listdir(class_path))\n",
    "        print(f\"{class_name}: {count} images\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "nbformat": 4,
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