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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Getting started"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import numpy as np\n",
-    "\n",
-    "import torch\n",
-    "import torchvision.transforms as transforms\n",
-    "import torchvision.models as models\n",
-    "from torchinfo import summary\n",
-    "\n",
-    "\n",
-    "from PIL import Image\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "%matplotlib inline\n",
-    "\n",
-    "dev = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "print(f'Using {dev} for inference')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load Caltech101 dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torchvision.datasets as datasets\n",
-    "\n",
-    "dataset_path = \"C:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Datasets\"\n",
-    "\n",
-    "dataset = datasets.Caltech101(root=\"C:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Datasets\", download=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Visualize a sample image from the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "\n",
-    "sample_image, _ = dataset.__getitem__(random.randint(0,len(dataset)))\n",
-    "plt.imshow(sample_image)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ResNet50 - Example classification"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Step 1: Load model\n",
-    "from torchvision.models import ResNet50_Weights\n",
-    "\n",
-    "weights = ResNet50_Weights.DEFAULT\n",
-    "model = models.resnet50(weights)\n",
-    "\n",
-    "if(torch.cuda.is_available()):\n",
-    "    model = model.to(dev)\n",
-    "\n",
-    "model.eval()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchvision.io import read_image\n",
-    "\n",
-    "# Step 2: Initialize the inference transforms\n",
-    "preprocess = weights.transforms()\n",
-    "\n",
-    "img = sample_image\n",
-    "# Step 3: Apply inference preprocessing transforms\n",
-    "batch = preprocess(img).unsqueeze(0)\n",
-    "\n",
-    "# (convert to CUDA tensor)\n",
-    "batch = batch.to(dev)\n",
-    "# Step 4: Use the model and print the predicted category\n",
-    "\n",
-    "prediction = model(batch).squeeze(0).softmax(0)\n",
-    "class_id = prediction.argmax().item()\n",
-    "score = prediction[class_id].item()\n",
-    "category_name = weights.meta[\"categories\"][class_id]\n",
-    "print(f\"{category_name}: {100 * score:.1f}%\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}