pytorch_model_deployment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a78201e-5901-484d-aede-8adfd9352917",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jamshid\\AppData\\Local\\Temp\\ipykernel_9368\\410869984.py:33: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n",
      "  color_cycle = plt.cm.get_cmap('tab10')  # Use a color map with different colors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "from helper_functions import plot_loss_curves  \n",
    "\n",
    "def load_results_from_directory(directory_path):\n",
    "    \"\"\"Load results from all JSON files in the specified directory.\"\"\"\n",
    "    results_dict = {}\n",
    "    for filename in os.listdir(directory_path):\n",
    "        if filename.endswith(\".json\"):  \n",
    "            filepath = os.path.join(directory_path, filename)\n",
    "            with open(filepath, 'r') as file:\n",
    "                results = json.load(file)\n",
    "                results_dict[filename] = results\n",
    "    return results_dict\n",
    "\n",
    "def plot_all_model_curves(directory_path):\n",
    "    \"\"\"Plots loss and accuracy curves for all models in the directory.\"\"\"\n",
    "    results_dict = load_results_from_directory(directory_path)\n",
    "\n",
    "    plt.figure(figsize=(15, 7))\n",
    "\n",
    "    # Plot loss\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.title(\"Loss\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "\n",
    "    # Plot accuracy\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.title(\"Accuracy\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "\n",
    "    color_cycle = plt.cm.get_cmap('tab10')  # Use a color map with different colors\n",
    "    color_index = 0\n",
    "\n",
    "    for model_name, results in results_dict.items():\n",
    "        epochs = range(len(results[\"train_loss\"]))\n",
    "        \n",
    "        # Use the same color for loss and accuracy for each model\n",
    "        color = color_cycle(color_index)\n",
    "        color_index = (color_index + 1) % 10  # Loop through color palette\n",
    "\n",
    "        # Plot loss\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.plot(epochs, results[\"train_loss\"], label=f\"{model_name} train_loss\", color=color)\n",
    "        plt.plot(epochs, results[\"test_loss\"], label=f\"{model_name} test_loss\", linestyle='--', color=color)\n",
    "\n",
    "        # Plot accuracy\n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.plot(epochs, results[\"train_acc\"], label=f\"{model_name} train_acc\", color=color)\n",
    "        plt.plot(epochs, results[\"test_acc\"], label=f\"{model_name} test_acc\", linestyle='--', color=color)\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "# Example usage:\n",
    "directory_path = \"results\"\n",
    "plot_all_model_curves(directory_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8662befd-96dc-41ed-be35-ed6f8306dcdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['efficientnet_b0_cifar10.pth',\n",
       " 'pretrained_efficientnet_b2_model.pth',\n",
       " 'pretrained_efficientnet_b4_model.pth',\n",
       " 'pretrained_efficientnet_v2_s_model.pth',\n",
       " 'pretrained_resnet_101_model.pth',\n",
       " 'pretrained_vit_b32_model.pth',\n",
       " 'pretrained_vit_b32_model_2.pth',\n",
       " 'pretrained_vit_model.pth',\n",
       " 'pretrained_vit_model_2.pth',\n",
       " 'resnet_cifar100.pth']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "models = os.listdir(\"models_food101\")\n",
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f340249-63c4-49ad-85c2-6add19e18b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "efficientnet_b0_cifar10.pth : 15 MB\n",
      "pretrained_efficientnet_b2_model.pth : 30 MB\n",
      "pretrained_efficientnet_b4_model.pth : 68 MB\n",
      "pretrained_efficientnet_v2_s_model.pth : 78 MB\n",
      "pretrained_resnet_101_model.pth : 163 MB\n",
      "pretrained_vit_b32_model.pth : 333 MB\n",
      "pretrained_vit_b32_model_2.pth : 333 MB\n",
      "pretrained_vit_model.pth : 327 MB\n",
      "pretrained_vit_model_2.pth : 327 MB\n",
      "resnet_cifar100.pth : 81 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get the model size in bytes then convert to megabytes\n",
    "models_dict = {}\n",
    "for i in range(len(models)):\n",
    "    models_dict[models[i]] = Path(\"models_food101\" + '/' +  models[i]).stat().st_size // (1024*1024)\n",
    "\n",
    "for k, v in models_dict.items():\n",
    "    print(f\"{k} : {v} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8e62670-6bd7-48a8-b4c8-82f1d5f9631c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from PIL import Image\n",
    "from timeit import default_timer as timer\n",
    "from tqdm.auto import tqdm\n",
    "from typing import List, Dict\n",
    "\n",
    "def pred_and_store(paths: List[pathlib.Path],\n",
    "                  model: torch.nn.Module,\n",
    "                  transform: torchvision.transforms,\n",
    "                  class_names: List[str],\n",
    "                   device: str = \"cuda\" if torch.cuda.is_available() else \"cpu\") -> List[Dict]:\n",
    "    # 2. Create an empty list to store prediction dictionaries\n",
    "    pred_list = []\n",
    "    # 3. loop through target paths\n",
    "    for path in tqdm(paths):\n",
    "        # 4. Create an empty list to store prediction information for each sample\n",
    "        pred_dict = {}\n",
    "\n",
    "        # 5. Get the sample path and ground truth class name\n",
    "        pred_dict['image_path'] = path\n",
    "        class_name = path.parent.stem\n",
    "        pred_dict['class_name'] = class_name\n",
    "\n",
    "        # 6. Start the prediction timer()\n",
    "        start_time = timer()\n",
    "\n",
    "        # 7. Open image path\n",
    "        img = Image.open(path)\n",
    "\n",
    "        # 8. Transform the image, add batch dimension and put image on target device\n",
    "        transformed_image = transform(img).unsqueeze(0).to(device)\n",
    "\n",
    "        # 9. Prepare model for inference by sending it to target device and turning on the eval() mode\n",
    "        model.to(device)\n",
    "        model.eval()\n",
    "\n",
    "        # 10 Get prediction probability, prediction label and prediction class\n",
    "        with torch.inference_mode():\n",
    "            pred_logit = model(transformed_image)\n",
    "            pred_prob = torch.softmax(pred_logit, dim=1)\n",
    "            pred_label = torch.argmax(pred_prob, dim=1)\n",
    "            pred_class = class_names[pred_label.cpu()]\n",
    "\n",
    "            # 11. Mkae sure things in the dict are on CPU ( required for instpecting predictions later on)\n",
    "            pred_dict['pred_prob'] = round(pred_prob.unsqueeze(0).max().cpu().item(), 3)\n",
    "            pred_dict['red_class'] = pred_class\n",
    "\n",
    "            # 12. End the timer to calculate time per pred\n",
    "            end_time = timer()\n",
    "            pred_dict[\"time_for_pred\"] = round(end_time-start_time, 4)\n",
    "\n",
    "        # 13. Does the pred match the true label?\n",
    "        pred_dict[\"correct\"] = class_name == pred_class\n",
    "        # 14. Add the dict to the list of preds\n",
    "        pred_list.append(pred_dict)\n",
    "\n",
    "    return pred_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d7dea7c-4de6-49ca-9572-59b6d2552edd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jamshid\\Desktop\\pytorch\\venv\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Jamshid\\Desktop\\pytorch\\venv\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataParallel(\n",
      "  (module): EfficientNet(\n",
      "    (features): Sequential(\n",
      "      (0): Conv2dNormActivation(\n",
      "        (0): Conv2d(3, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (2): SiLU(inplace=True)\n",
      "      )\n",
      "      (1): Sequential(\n",
      "        (0): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)\n",
      "              (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(12, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (2): Conv2dNormActivation(\n",
      "              (0): Conv2d(48, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.0, mode=row)\n",
      "        )\n",
      "        (1): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=24, bias=False)\n",
      "              (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(24, 6, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(6, 24, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (2): Conv2dNormActivation(\n",
      "              (0): Conv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.00625, mode=row)\n",
      "        )\n",
      "      )\n",
      "      (2): Sequential(\n",
      "        (0): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
      "              (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.0125, mode=row)\n",
      "        )\n",
      "        (1): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.018750000000000003, mode=row)\n",
      "        )\n",
      "        (2): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.025, mode=row)\n",
      "        )\n",
      "        (3): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.03125, mode=row)\n",
      "        )\n",
      "      )\n",
      "      (3): Sequential(\n",
      "        (0): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(192, 192, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=192, bias=False)\n",
      "              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(192, 8, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(8, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(192, 56, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.037500000000000006, mode=row)\n",
      "        )\n",
      "        (1): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(336, 336, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=336, bias=False)\n",
      "              (1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(336, 14, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(14, 336, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(336, 56, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.043750000000000004, mode=row)\n",
      "        )\n",
      "        (2): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(336, 336, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=336, bias=False)\n",
      "              (1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(336, 14, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(14, 336, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(336, 56, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.05, mode=row)\n",
      "        )\n",
      "        (3): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(336, 336, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=336, bias=False)\n",
      "              (1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(336, 14, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(14, 336, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(336, 56, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.05625, mode=row)\n",
      "        )\n",
      "      )\n",
      "      (4): Sequential(\n",
      "        (0): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(336, 336, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=336, bias=False)\n",
      "              (1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(336, 14, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(14, 336, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(336, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.0625, mode=row)\n",
      "        )\n",
      "        (1): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=672, bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.06875, mode=row)\n",
      "        )\n",
      "        (2): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=672, bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.07500000000000001, mode=row)\n",
      "        )\n",
      "        (3): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=672, bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.08125, mode=row)\n",
      "        )\n",
      "        (4): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=672, bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.08750000000000001, mode=row)\n",
      "        )\n",
      "        (5): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=672, bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.09375, mode=row)\n",
      "        )\n",
      "      )\n",
      "      (5): Sequential(\n",
      "        (0): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n",
      "              (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(672, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.1, mode=row)\n",
      "        )\n",
      "        (1): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.10625000000000001, mode=row)\n",
      "        )\n",
      "        (2): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.1125, mode=row)\n",
      "        )\n",
      "        (3): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.11875000000000001, mode=row)\n",
      "        )\n",
      "        (4): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.125, mode=row)\n",
      "        )\n",
      "        (5): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.13125, mode=row)\n",
      "        )\n",
      "      )\n",
      "      (6): Sequential(\n",
      "        (0): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=960, bias=False)\n",
      "              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(960, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.1375, mode=row)\n",
      "        )\n",
      "        (1): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 1632, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1632, bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(1632, 68, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(68, 1632, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.14375000000000002, mode=row)\n",
      "        )\n",
      "        (2): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 1632, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1632, bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(1632, 68, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(68, 1632, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.15000000000000002, mode=row)\n",
      "        )\n",
      "        (3): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 1632, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1632, bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(1632, 68, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(68, 1632, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.15625, mode=row)\n",
      "        )\n",
      "        (4): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 1632, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1632, bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(1632, 68, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(68, 1632, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.1625, mode=row)\n",
      "        )\n",
      "        (5): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 1632, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1632, bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(1632, 68, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(68, 1632, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.16875, mode=row)\n",
      "        )\n",
      "        (6): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 1632, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1632, bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(1632, 68, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(68, 1632, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.17500000000000002, mode=row)\n",
      "        )\n",
      "        (7): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 1632, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1632, bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(1632, 68, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(68, 1632, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.18125000000000002, mode=row)\n",
      "        )\n",
      "      )\n",
      "      (7): Sequential(\n",
      "        (0): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 1632, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1632, bias=False)\n",
      "              (1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(1632, 68, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(68, 1632, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(1632, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.1875, mode=row)\n",
      "        )\n",
      "        (1): MBConv(\n",
      "          (block): Sequential(\n",
      "            (0): Conv2dNormActivation(\n",
      "              (0): Conv2d(448, 2688, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(2688, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (1): Conv2dNormActivation(\n",
      "              (0): Conv2d(2688, 2688, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2688, bias=False)\n",
      "              (1): BatchNorm2d(2688, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "              (2): SiLU(inplace=True)\n",
      "            )\n",
      "            (2): SqueezeExcitation(\n",
      "              (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "              (fc1): Conv2d(2688, 112, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (fc2): Conv2d(112, 2688, kernel_size=(1, 1), stride=(1, 1))\n",
      "              (activation): SiLU(inplace=True)\n",
      "              (scale_activation): Sigmoid()\n",
      "            )\n",
      "            (3): Conv2dNormActivation(\n",
      "              (0): Conv2d(2688, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (stochastic_depth): StochasticDepth(p=0.19375, mode=row)\n",
      "        )\n",
      "      )\n",
      "      (8): Conv2dNormActivation(\n",
      "        (0): Conv2d(448, 1792, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (2): SiLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "    (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
      "    (classifier): Sequential(\n",
      "      (0): Dropout(p=0.4, inplace=True)\n",
      "      (1): Linear(in_features=1792, out_features=101, bias=True)\n",
      "    )\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "\n",
    "# Initialize EfficientNet-B4 model\n",
    "model = torchvision.models.efficientnet_b4(pretrained=False)\n",
    "\n",
    "# Modify the classifier head to match 101 classes (for Food101)\n",
    "model.classifier[1] = nn.Linear(in_features=1792, out_features=101)\n",
    "\n",
    "# Use DataParallel to match the saved state dict\n",
    "model = torch.nn.DataParallel(model)\n",
    "\n",
    "# Load the saved weights\n",
    "model.load_state_dict(torch.load(\"models_food101/pretrained_efficientnet_b4_model.pth\"))\n",
    "\n",
    "# Move model to the device (GPU or CPU)\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "model = model.to(device)\n",
    "\n",
    "# Set the model to evaluation mode for inference\n",
    "model.eval()\n",
    "\n",
    "print(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8e5f520-2e33-4c23-86a8-03827fe8fdd5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple_pie',\n",
       " 'baby_back_ribs',\n",
       " 'baklava',\n",
       " 'beef_carpaccio',\n",
       " 'beef_tartare',\n",
       " 'beet_salad',\n",
       " 'beignets',\n",
       " 'bibimbap',\n",
       " 'bread_pudding',\n",
       " 'breakfast_burrito',\n",
       " 'bruschetta',\n",
       " 'caesar_salad',\n",
       " 'cannoli',\n",
       " 'caprese_salad',\n",
       " 'carrot_cake',\n",
       " 'ceviche',\n",
       " 'cheese_plate',\n",
       " 'cheesecake',\n",
       " 'chicken_curry',\n",
       " 'chicken_quesadilla',\n",
       " 'chicken_wings',\n",
       " 'chocolate_cake',\n",
       " 'chocolate_mousse',\n",
       " 'churros',\n",
       " 'clam_chowder',\n",
       " 'club_sandwich',\n",
       " 'crab_cakes',\n",
       " 'creme_brulee',\n",
       " 'croque_madame',\n",
       " 'cup_cakes',\n",
       " 'deviled_eggs',\n",
       " 'donuts',\n",
       " 'dumplings',\n",
       " 'edamame',\n",
       " 'eggs_benedict',\n",
       " 'escargots',\n",
       " 'falafel',\n",
       " 'filet_mignon',\n",
       " 'fish_and_chips',\n",
       " 'foie_gras',\n",
       " 'french_fries',\n",
       " 'french_onion_soup',\n",
       " 'french_toast',\n",
       " 'fried_calamari',\n",
       " 'fried_rice',\n",
       " 'frozen_yogurt',\n",
       " 'garlic_bread',\n",
       " 'gnocchi',\n",
       " 'greek_salad',\n",
       " 'grilled_cheese_sandwich',\n",
       " 'grilled_salmon',\n",
       " 'guacamole',\n",
       " 'gyoza',\n",
       " 'hamburger',\n",
       " 'hot_and_sour_soup',\n",
       " 'hot_dog',\n",
       " 'huevos_rancheros',\n",
       " 'hummus',\n",
       " 'ice_cream',\n",
       " 'lasagna',\n",
       " 'lobster_bisque',\n",
       " 'lobster_roll_sandwich',\n",
       " 'macaroni_and_cheese',\n",
       " 'macarons',\n",
       " 'miso_soup',\n",
       " 'mussels',\n",
       " 'nachos',\n",
       " 'omelette',\n",
       " 'onion_rings',\n",
       " 'oysters',\n",
       " 'pad_thai',\n",
       " 'paella',\n",
       " 'pancakes',\n",
       " 'panna_cotta',\n",
       " 'peking_duck',\n",
       " 'pho',\n",
       " 'pizza',\n",
       " 'pork_chop',\n",
       " 'poutine',\n",
       " 'prime_rib',\n",
       " 'pulled_pork_sandwich',\n",
       " 'ramen',\n",
       " 'ravioli',\n",
       " 'red_velvet_cake',\n",
       " 'risotto',\n",
       " 'samosa',\n",
       " 'sashimi',\n",
       " 'scallops',\n",
       " 'seaweed_salad',\n",
       " 'shrimp_and_grits',\n",
       " 'spaghetti_bolognese',\n",
       " 'spaghetti_carbonara',\n",
       " 'spring_rolls',\n",
       " 'steak',\n",
       " 'strawberry_shortcake',\n",
       " 'sushi',\n",
       " 'tacos',\n",
       " 'takoyaki',\n",
       " 'tiramisu',\n",
       " 'tuna_tartare',\n",
       " 'waffles']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchvision import transforms\n",
    "from going_modular import data_setup\n",
    "\n",
    "train_dir = \"data/test\"\n",
    "test_dir = \"data/test\"\n",
    "\n",
    "pretrained_efficientnetb4_weights = torchvision.models.EfficientNet_B4_Weights.DEFAULT\n",
    "efficientnetb4_transforms = transforms.Compose([\n",
    "    transforms.Resize((380, 380)),  # Increase resolution to 380x380\n",
    "    pretrained_efficientnetb4_weights.transforms(),  # Pre-trained EfficientNet-B4 normalization\n",
    "])\n",
    "\n",
    "train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
    "                                                                              test_dir=test_dir,\n",
    "                                                                              transform=efficientnetb4_transforms,\n",
    "                                                                              batch_size=256)\n",
    "class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69995bf4-f5d3-4cf2-803b-a54cf0179c97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Finding all filepaths ending with '.jpg' in directory: data/test\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64cd96975c0148839d45732cb5167ef7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get all test data paths\n",
    "print(f\"[INFO] Finding all filepaths ending with '.jpg' in directory: {test_dir}\")\n",
    "test_data_paths = list(Path(test_dir).glob(\"*/*.jpg\"))\n",
    "\n",
    "effnetb4_test_pred_dicts = pred_and_store(paths=test_data_paths,\n",
    "                                         model=model,\n",
    "                                         transform=efficientnetb4_transforms,\n",
    "                                         class_names=class_names,\n",
    "                                         device='cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f8df7deb-0789-4ed8-b398-f46af6794323",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'image_path': WindowsPath('data/test/apple_pie/1011328.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.976,\n",
       "  'red_class': 'gyoza',\n",
       "  'time_for_pred': 0.3989,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/101251.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.031,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1034399.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.995,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.034,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/103801.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.034,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1047447.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.993,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0299,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1068632.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.829,\n",
       "  'red_class': 'chocolate_cake',\n",
       "  'time_for_pred': 0.0294,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/110043.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.99,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0257,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1106961.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.989,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0279,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1113017.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.993,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0285,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/116705.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.999,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.028,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1167170.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.996,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0258,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1174330.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.692,\n",
       "  'red_class': 'fried_calamari',\n",
       "  'time_for_pred': 0.027,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1180600.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.028,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1235282.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.925,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.027,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1269202.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.904,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1272958.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0249,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1295996.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.952,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0247,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/129668.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.583,\n",
       "  'red_class': 'bread_pudding',\n",
       "  'time_for_pred': 0.029,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1321400.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.857,\n",
       "  'red_class': 'grilled_cheese_sandwich',\n",
       "  'time_for_pred': 0.0263,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1327259.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.028,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1332030.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.027,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1364847.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0284,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1375404.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.994,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0256,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1398774.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.997,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1420999.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.969,\n",
       "  'red_class': 'ice_cream',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1429866.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0283,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1444702.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0264,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1461580.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.839,\n",
       "  'red_class': 'baklava',\n",
       "  'time_for_pred': 0.0256,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1469191.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.839,\n",
       "  'red_class': 'baklava',\n",
       "  'time_for_pred': 0.026,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1510125.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.877,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0264,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1510930.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0273,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1579622.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.9,\n",
       "  'red_class': 'french_onion_soup',\n",
       "  'time_for_pred': 0.0304,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1579774.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0262,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1617793.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.915,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0276,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1626685.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.98,\n",
       "  'red_class': 'grilled_cheese_sandwich',\n",
       "  'time_for_pred': 0.0247,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1629072.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.994,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0259,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1657587.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0245,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1674492.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0268,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/168971.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.797,\n",
       "  'red_class': 'cannoli',\n",
       "  'time_for_pred': 0.025,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1771374.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.631,\n",
       "  'red_class': 'bread_pudding',\n",
       "  'time_for_pred': 0.0356,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1773419.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.785,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0247,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1774036.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.871,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0294,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1815947.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.58,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0299,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1847621.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0344,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1854241.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0278,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1889575.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.891,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0295,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1899005.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.974,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0363,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/1919827.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0285,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2034818.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.961,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.026,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2038171.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.994,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0276,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2084963.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.995,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0256,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2109273.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.981,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0254,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2110985.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.481,\n",
       "  'red_class': 'carrot_cake',\n",
       "  'time_for_pred': 0.0253,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2113478.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.641,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0263,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2130269.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0273,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2175701.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.949,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0337,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2189388.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.81,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0287,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2200733.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.917,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.029,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2206034.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0271,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2206649.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.778,\n",
       "  'red_class': 'grilled_cheese_sandwich',\n",
       "  'time_for_pred': 0.0257,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2214366.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.98,\n",
       "  'red_class': 'foie_gras',\n",
       "  'time_for_pred': 0.025,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2218771.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0265,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/224383.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.714,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0254,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/224785.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.99,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2254489.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0257,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2271474.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0268,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/229142.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.903,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0283,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2298195.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.752,\n",
       "  'red_class': 'frozen_yogurt',\n",
       "  'time_for_pred': 0.0289,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2304784.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.968,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0286,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2334907.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0273,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2338398.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.965,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2368252.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.777,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.025,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2373323.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.684,\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2374771.jpg'),\n",
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       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/2380340.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2395631.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2402925.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2407532.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2440054.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2445712.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2446133.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2446500.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2496124.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.849,\n",
       "  'red_class': 'apple_pie',\n",
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       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/250066.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2511157.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2517059.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2523219.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2523928.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2553659.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2559009.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2566460.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/257377.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2576737.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2586042.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2596943.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2601847.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2610524.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2663191.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2669146.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2680614.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2698889.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2737512.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2754101.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2766017.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2791334.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/280007.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2848506.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2878249.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2881877.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2909139.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2911777.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/2945592.jpg'),\n",
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       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.816,\n",
       "  'red_class': 'carrot_cake',\n",
       "  'time_for_pred': 0.0271,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3635782.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'tiramisu',\n",
       "  'time_for_pred': 0.031,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3647026.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.356,\n",
       "  'red_class': 'cheesecake',\n",
       "  'time_for_pred': 0.028,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3676950.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.714,\n",
       "  'red_class': 'beignets',\n",
       "  'time_for_pred': 0.0281,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3691035.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.986,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3708762.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.414,\n",
       "  'red_class': 'pancakes',\n",
       "  'time_for_pred': 0.0268,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3720779.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.999,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0298,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3727785.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.427,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0262,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3746599.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.479,\n",
       "  'red_class': 'tuna_tartare',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3779150.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0248,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3800561.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0242,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3809728.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0275,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3825794.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0419,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3829004.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.869,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0291,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3860485.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.957,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0246,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/38795.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.689,\n",
       "  'red_class': 'grilled_cheese_sandwich',\n",
       "  'time_for_pred': 0.0311,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/3915901.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0253,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/406682.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0316,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/407267.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.906,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0276,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/416233.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0252,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/440497.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.972,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0252,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/451719.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.609,\n",
       "  'red_class': 'eggs_benedict',\n",
       "  'time_for_pred': 0.0246,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/455977.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0288,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/466304.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.986,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0305,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/484947.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.51,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0339,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/486170.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0256,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/495046.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.965,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0266,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/507349.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.987,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0294,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/532019.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.993,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.027,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/532974.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.793,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0266,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/590327.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/646264.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.608,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0273,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/661475.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0281,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/668694.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.986,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0275,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/688983.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'french_toast',\n",
       "  'time_for_pred': 0.0269,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/693210.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.98,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0287,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/801869.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.842,\n",
       "  'red_class': 'french_toast',\n",
       "  'time_for_pred': 0.0248,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/812047.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0266,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/827575.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0272,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/829175.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.613,\n",
       "  'red_class': 'bread_pudding',\n",
       "  'time_for_pred': 0.0254,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/829830.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.505,\n",
       "  'red_class': 'waffles',\n",
       "  'time_for_pred': 0.0248,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/829949.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.583,\n",
       "  'red_class': 'bruschetta',\n",
       "  'time_for_pred': 0.0249,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/83981.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.999,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0258,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/861244.jpg'),\n",
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       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0318,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/867502.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/879842.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.658,\n",
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       "  'time_for_pred': 0.0257,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/885848.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/886793.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.65,\n",
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       "  'time_for_pred': 0.0282,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/904832.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.996,\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/963140.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/981895.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/986844.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       "  'time_for_pred': 0.0363,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/99556.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
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       " {'image_path': WindowsPath('data/test/apple_pie/997950.jpg'),\n",
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       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0289,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/baby_back_ribs/1005066.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'red_class': 'baby_back_ribs',\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/1022479.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/1058654.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'time_for_pred': 0.0349,\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/1074480.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/1098606.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/112142.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/1138370.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/1381124.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/1427390.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/2517831.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/2576256.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/26834.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/2687000.jpg'),\n",
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       "  'class_name': 'baby_back_ribs',\n",
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       "  'red_class': 'baby_back_ribs',\n",
       "  'time_for_pred': 0.0316,\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/3828218.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'red_class': 'baby_back_ribs',\n",
       "  'time_for_pred': 0.0248,\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/384960.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'red_class': 'pork_chop',\n",
       "  'time_for_pred': 0.0282,\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/3863637.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'red_class': 'baby_back_ribs',\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/3873682.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
       "  'pred_prob': 0.998,\n",
       "  'red_class': 'pulled_pork_sandwich',\n",
       "  'time_for_pred': 0.0304,\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/3890499.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'red_class': 'baby_back_ribs',\n",
       "  'time_for_pred': 0.0274,\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/426980.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/464659.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'time_for_pred': 0.0257,\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/470646.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'red_class': 'baby_back_ribs',\n",
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       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/baby_back_ribs/501334.jpg'),\n",
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       "  'red_class': 'baby_back_ribs',\n",
       "  'time_for_pred': 0.0334,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/baby_back_ribs/519489.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'red_class': 'baby_back_ribs',\n",
       "  'time_for_pred': 0.0362,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/baby_back_ribs/54203.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       "  'time_for_pred': 0.0244,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/baby_back_ribs/543585.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/546242.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/563715.jpg'),\n",
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       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/baby_back_ribs/586642.jpg'),\n",
       "  'class_name': 'baby_back_ribs',\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/593493.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/611849.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/620997.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/626550.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/652170.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/748033.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/787250.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/824318.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/baby_back_ribs/87031.jpg'),\n",
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       "  'red_class': 'panna_cotta',\n",
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       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/167712.jpg'),\n",
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       "  'red_class': 'beef_tartare',\n",
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       " {'image_path': WindowsPath('data/test/beef_tartare/1685187.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/beef_tartare/1697360.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/beef_tartare/1892973.jpg'),\n",
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       " {'image_path': WindowsPath('data/test/beef_tartare/2956362.jpg'),\n",
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       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.93,\n",
       "  'red_class': 'tuna_tartare',\n",
       "  'time_for_pred': 0.0242,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/531830.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.976,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0244,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/537438.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.632,\n",
       "  'red_class': 'beet_salad',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/544934.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.781,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0271,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/578209.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0252,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/648126.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.992,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0265,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/670434.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.965,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0249,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/683016.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.982,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0254,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/700095.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.999,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0274,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/700297.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0251,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/708184.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.995,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0258,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/710734.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0366,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/718117.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0264,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/723452.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.719,\n",
       "  'red_class': 'crab_cakes',\n",
       "  'time_for_pred': 0.0246,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/7307.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0249,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/731281.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.982,\n",
       "  'red_class': 'grilled_salmon',\n",
       "  'time_for_pred': 0.0261,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/742212.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0265,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/77161.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0243,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/812907.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0266,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/868601.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0264,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/887280.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.56,\n",
       "  'red_class': 'tuna_tartare',\n",
       "  'time_for_pred': 0.0253,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/901569.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.998,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.025,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/907414.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0265,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/926143.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 0.997,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0287,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/948352.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0327,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/956509.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.025,\n",
       "  'correct': True},\n",
       " {'image_path': WindowsPath('data/test/beef_tartare/984112.jpg'),\n",
       "  'class_name': 'beef_tartare',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'beef_tartare',\n",
       "  'time_for_pred': 0.0252,\n",
       "  'correct': True},\n",
       " ...]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effnetb4_test_pred_dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "af628ae1-1f6a-4f4c-86ba-db09c674cf36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20200"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(effnetb4_test_pred_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "263a5da6-9dd3-4e3c-8de8-accfdd2b2611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image_path': WindowsPath('data/test/apple_pie/1011328.jpg'),\n",
       " 'class_name': 'apple_pie',\n",
       " 'pred_prob': 0.976,\n",
       " 'red_class': 'gyoza',\n",
       " 'time_for_pred': 0.3989,\n",
       " 'correct': False}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effnetb4_test_pred_dicts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "54af5efb-d983-44ef-b403-f90a4453f8e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "wrong_preds = 0\n",
    "for i in range(len(effnetb4_test_pred_dicts)):\n",
    "    if effnetb4_test_pred_dicts[i]['correct'] == False:\n",
    "        wrong_preds += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e631d4d2-582d-45ad-97c7-dd22af55f97f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7313540912382333"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len(effnetb4_test_pred_dicts) / wrong_preds) / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2054cb8-389b-44f7-9a46-8f0e899de6ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'image_path': WindowsPath('data/test/apple_pie/1011328.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.976,\n",
       "  'red_class': 'gyoza',\n",
       "  'time_for_pred': 0.3989,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/101251.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.031,\n",
       "  'correct': True}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effnetb4_test_pred_dicts[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc917f8e-332a-4acb-8096-84612275deb2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>class_name</th>\n",
       "      <th>pred_prob</th>\n",
       "      <th>red_class</th>\n",
       "      <th>time_for_pred</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data\\test\\apple_pie\\1011328.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.976</td>\n",
       "      <td>gyoza</td>\n",
       "      <td>0.3989</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data\\test\\apple_pie\\101251.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>1.000</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data\\test\\apple_pie\\1034399.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.995</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.0340</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data\\test\\apple_pie\\103801.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>1.000</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.0340</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data\\test\\apple_pie\\1047447.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.993</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.0299</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        image_path class_name  pred_prob  red_class  \\\n",
       "0  data\\test\\apple_pie\\1011328.jpg  apple_pie      0.976      gyoza   \n",
       "1   data\\test\\apple_pie\\101251.jpg  apple_pie      1.000  apple_pie   \n",
       "2  data\\test\\apple_pie\\1034399.jpg  apple_pie      0.995  apple_pie   \n",
       "3   data\\test\\apple_pie\\103801.jpg  apple_pie      1.000  apple_pie   \n",
       "4  data\\test\\apple_pie\\1047447.jpg  apple_pie      0.993  apple_pie   \n",
       "\n",
       "   time_for_pred  correct  \n",
       "0         0.3989    False  \n",
       "1         0.0310     True  \n",
       "2         0.0340     True  \n",
       "3         0.0340     True  \n",
       "4         0.0299     True  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "effnetb4_test_pred_df = pd.DataFrame(effnetb4_test_pred_dicts)\n",
    "effnetb4_test_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0f5c7e9-f9a9-4083-98ef-50f1625467c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "correct\n",
       "True     17438\n",
       "False     2762\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effnetb4_test_pred_df.correct.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a22ebb8-de3f-479b-8ca0-a7661fe31cab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EffNetB4 average time per prediction:0.0267 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Find average time period prediction\n",
    "effnetb4_average_time_per_pred = round(effnetb4_test_pred_df.time_for_pred.mean(), 4)\n",
    "print(f\"EffNetB4 average time per prediction:{effnetb4_average_time_per_pred} seconds.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ab2f304-31d7-4da2-99ca-25d7a40e1188",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87.1%\n"
     ]
    }
   ],
   "source": [
    "print(f\"{round(2199 / 2525, 3) * 100}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "319efd4f-0448-4f1a-be79-d9b6908306db",
   "metadata": {},
   "source": [
    "# Prediction test run 2 | with 200 test images for each class\n",
    "# Total : 20,200 test images for 101 classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8368e1e0-ef49-48d1-878a-cfa090c2c207",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Finding all filepaths ending with '.jpg' in directory: data/test\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c0dd40d3fa64da39f7c788a8e6c779e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Get all test data paths\n",
    "print(f\"[INFO] Finding all filepaths ending with '.jpg' in directory: {test_dir}\")\n",
    "test_data_paths = list(Path(test_dir).glob(\"*/*.jpg\"))\n",
    "\n",
    "effnetb4_test_pred_dicts_2 = pred_and_store(paths=test_data_paths,\n",
    "                                         model=model,\n",
    "                                         transform=efficientnetb4_transforms,\n",
    "                                         class_names=class_names,\n",
    "                                         device='cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea1e4605-7dbd-4d58-867b-d4c42a72b3d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'image_path': WindowsPath('data/test/apple_pie/1011328.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 0.976,\n",
       "  'red_class': 'gyoza',\n",
       "  'time_for_pred': 0.0295,\n",
       "  'correct': False},\n",
       " {'image_path': WindowsPath('data/test/apple_pie/101251.jpg'),\n",
       "  'class_name': 'apple_pie',\n",
       "  'pred_prob': 1.0,\n",
       "  'red_class': 'apple_pie',\n",
       "  'time_for_pred': 0.0284,\n",
       "  'correct': True}]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effnetb4_test_pred_dicts_2[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3daa7509-19d7-484c-a531-74708581d984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>class_name</th>\n",
       "      <th>pred_prob</th>\n",
       "      <th>red_class</th>\n",
       "      <th>time_for_pred</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data\\test\\apple_pie\\1011328.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.976</td>\n",
       "      <td>gyoza</td>\n",
       "      <td>0.0295</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data\\test\\apple_pie\\101251.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>1.000</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.0284</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data\\test\\apple_pie\\1034399.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.995</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.0282</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data\\test\\apple_pie\\103801.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>1.000</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.0270</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data\\test\\apple_pie\\1047447.jpg</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.993</td>\n",
       "      <td>apple_pie</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        image_path class_name  pred_prob  red_class  \\\n",
       "0  data\\test\\apple_pie\\1011328.jpg  apple_pie      0.976      gyoza   \n",
       "1   data\\test\\apple_pie\\101251.jpg  apple_pie      1.000  apple_pie   \n",
       "2  data\\test\\apple_pie\\1034399.jpg  apple_pie      0.995  apple_pie   \n",
       "3   data\\test\\apple_pie\\103801.jpg  apple_pie      1.000  apple_pie   \n",
       "4  data\\test\\apple_pie\\1047447.jpg  apple_pie      0.993  apple_pie   \n",
       "\n",
       "   time_for_pred  correct  \n",
       "0         0.0295    False  \n",
       "1         0.0284     True  \n",
       "2         0.0282     True  \n",
       "3         0.0270     True  \n",
       "4         0.0331     True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "effnetb4_test_pred_df_2= pd.DataFrame(effnetb4_test_pred_dicts_2)\n",
    "effnetb4_test_pred_df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0c6bd5ad-c0dc-4724-8b86-f9bb59b8bada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20200"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(effnetb4_test_pred_dicts_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8980e9e6-99fe-4d22-be95-1d187dd56caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "correct\n",
       "True     17438\n",
       "False     2762\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effnetb4_test_pred_df_2.correct.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b17af32c-2eb5-4dff-b150-43e89c7804cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.3%\n"
     ]
    }
   ],
   "source": [
    "print(f\"{round(17438 / len(effnetb4_test_pred_dicts_2), 3) * 100}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "341d4b1e-dbda-483c-b400-3a47507f5335",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EffNetB4 average time per prediction:0.0265 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Find average time period prediction\n",
    "effnetb4_average_time_per_pred_2 = round(effnetb4_test_pred_df_2.time_for_pred.mean(), 4)\n",
    "print(f\"EffNetB4 average time per prediction:{effnetb4_average_time_per_pred_2} seconds.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99243bf5-26b9-447f-94d8-2fae7ce81171",
   "metadata": {},
   "outputs": [],
   "source": [
    "effnet_b4_results = {\n",
    "    \"train_loss\": [\n",
    "        1.64002931762386,\n",
    "        0.8354565678416072,\n",
    "        0.6445213260280119,\n",
    "        0.5361178366316331,\n",
    "        0.465178761077491,\n",
    "        0.4181656831422368,\n",
    "        0.37720189686562566,\n",
    "        0.185924747606387,\n",
    "        0.11708441992114121,\n",
    "        0.0907826023050458,\n",
    "        0.07709137019323739,\n",
    "        0.06755708877274112,\n",
    "        0.062242068325144215,\n",
    "        0.055947171694070506,\n",
    "        0.046835999435594154\n",
    "    ],\n",
    "    \"train_acc\": [\n",
    "        0.5910619858989424,\n",
    "        0.7745668010061693,\n",
    "        0.820092744748825,\n",
    "        0.8470599845769683,\n",
    "        0.8641000247870153,\n",
    "        0.8774845540173325,\n",
    "        0.8880362487147474,\n",
    "        0.9439764110237955,\n",
    "        0.9651545057285547,\n",
    "        0.9719508528569331,\n",
    "        0.9765064997062278,\n",
    "        0.9796152045387779,\n",
    "        0.9810536547076968,\n",
    "        0.982475924463866,\n",
    "        0.9858383519388954\n",
    "    ],\n",
    "    \"test_loss\": [\n",
    "        0.6893176714880298,\n",
    "        0.6091981571581628,\n",
    "        0.583870479931133,\n",
    "        0.6200098804753236,\n",
    "        0.6343839331106707,\n",
    "        0.6671971629063288,\n",
    "        0.6501466177328669,\n",
    "        0.579550165871177,\n",
    "        0.6048787957941643,\n",
    "        0.6330661965590535,\n",
    "        0.6449009741496559,\n",
    "        0.6698759831864425,\n",
    "        0.6869810336466992,\n",
    "        0.6894769415710912,\n",
    "        0.7016165668163636\n",
    "    ],\n",
    "    \"test_acc\": [\n",
    "        0.8053382981045019,\n",
    "        0.8295089202207258,\n",
    "        0.8335564362763437,\n",
    "        0.8286471972814566,\n",
    "        0.8303579779274223,\n",
    "        0.8224485401858087,\n",
    "        0.8257463718979924,\n",
    "        0.8608237077565782,\n",
    "        0.8623980935902232,\n",
    "        0.8619742954233696,\n",
    "        0.86223149706946,\n",
    "        0.8621652481606186,\n",
    "        0.8616980959284201,\n",
    "        0.8615962869435091,\n",
    "        0.8624414476555681\n",
    "    ],\n",
    "    \"training_time\": \"1:12:16.003597\",\n",
    "    \"batch_size\": 256,\n",
    "    \"epochs\": 15\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f86e4438-861c-408c-a02e-dc1c6ec8c41e",
   "metadata": {},
   "outputs": [],
   "source": [
    "effnetb4_stats = {\"test_loss\": round(effnet_b4_results[\"test_loss\"][-1], 3),\n",
    "             \"test_acc\": round(effnet_b4_results[\"test_acc\"][-1], 3)*100,\n",
    "             \"model_size (MB)\": 30}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e9dccea7-efe3-446e-80a3-88935b300794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.702,\n",
       " 'test_acc': 86.2,\n",
       " 'model_size (MB)': 30,\n",
       " 'time_per_pred_gpu': 0.027}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effnetb4_stats[\"time_per_pred_gpu\"] = round(effnetb4_average_time_per_pred,3)\n",
    "effnetb4_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ed0e14-c320-470e-958c-8cfe41547c04",
   "metadata": {},
   "source": [
    "# Visualizing the speed vs. performance tradeoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "81b6d11b-7874-41b9-9878-11f3c0d72eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ..........."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa9c25a2-0e75-4921-b25a-5a30e3b05692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradio version: 4.44.0\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "\n",
    "print(f\"Gradio version: {gr.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "214ed1f3-e968-4dda-9196-05b85207478b",
   "metadata": {},
   "source": [
    "# It's time to see the food vision model in the wild"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aec42189-986e-48ad-b4e6-4a2026dd6871",
   "metadata": {},
   "outputs": [],
   "source": [
    "effnetb4 = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2351a65f-ec1c-4d85-a0a8-9acdd1ca4c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Compose(\n",
       "    Resize(size=(380, 380), interpolation=bilinear, max_size=None, antialias=True)\n",
       "    ImageClassification(\n",
       "    crop_size=[380]\n",
       "    resize_size=[384]\n",
       "    mean=[0.485, 0.456, 0.406]\n",
       "    std=[0.229, 0.224, 0.225]\n",
       "    interpolation=InterpolationMode.BICUBIC\n",
       ")\n",
       ")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "efficientnetb4_transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "82e7fc9d-12fb-4429-a053-8d197cce00bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "582212f2-44da-4335-9695-447a79304bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('data/test/apple_pie/1011328.jpg'),\n",
       " WindowsPath('data/test/apple_pie/101251.jpg'),\n",
       " WindowsPath('data/test/apple_pie/1034399.jpg'),\n",
       " WindowsPath('data/test/apple_pie/103801.jpg'),\n",
       " WindowsPath('data/test/apple_pie/1047447.jpg')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_paths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e48136d2-005e-4586-938a-03e7d44d5b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Tuple, Dict\n",
    "\n",
    "def predict(img) -> Tuple[Dict, float]:\n",
    "    \"\"\"Transforms and performs a prediction on img and returns prediction and time taken.\"\"\"\n",
    "    start_time = timer()\n",
    "    img = efficientnetb4_transforms(img).unsqueeze(0)\n",
    "\n",
    "    effnetb4.eval()\n",
    "    with torch.inference_mode():\n",
    "        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities\n",
    "        pred_probs = torch.softmax(effnetb4(img), dim=1)\n",
    "\n",
    "    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)\n",
    "    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}\n",
    "\n",
    "    # Calculate the prediction time\n",
    "    pred_time = round(timer() - start_time, 3)\n",
    "\n",
    "    # Return the prediction dictionary and prediction time \n",
    "    return pred_labels_and_probs, pred_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d735cebb-4def-4936-a1d2-8784c0d9b8da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Predicting on image path: data\\test\\lasagna\\134578.jpg\n",
      "\n",
      "Prediction label and probability dictionary: \n",
      "{'apple_pie': 2.1331786337341896e-10, 'baby_back_ribs': 5.7052261581702624e-08, 'baklava': 1.6957161369512974e-11, 'beef_carpaccio': 0.00022745240130461752, 'beef_tartare': 4.581664470038049e-08, 'beet_salad': 6.368261438183254e-07, 'beignets': 1.8848572480578696e-08, 'bibimbap': 1.4920026325526692e-09, 'bread_pudding': 1.1009796407890349e-09, 'breakfast_burrito': 1.752509206198738e-06, 'bruschetta': 1.6695900058039115e-06, 'caesar_salad': 7.057938944399211e-08, 'cannoli': 1.1251062304040715e-09, 'caprese_salad': 9.053856047103181e-06, 'carrot_cake': 2.1102171399167702e-10, 'ceviche': 8.408494522882393e-07, 'cheese_plate': 6.184226908345636e-09, 'cheesecake': 1.5328938118841506e-10, 'chicken_curry': 0.9945980310440063, 'chicken_quesadilla': 1.1911181445611874e-06, 'chicken_wings': 2.6686143428378273e-07, 'chocolate_cake': 1.065203264136172e-12, 'chocolate_mousse': 9.015548863011702e-10, 'churros': 1.24095386744294e-10, 'clam_chowder': 7.549605385293034e-09, 'club_sandwich': 1.6808312519600577e-09, 'crab_cakes': 7.157653453759849e-05, 'creme_brulee': 8.176423982320102e-09, 'croque_madame': 1.520745840366544e-08, 'cup_cakes': 8.203447686774834e-13, 'deviled_eggs': 6.070448677064633e-08, 'donuts': 1.946735411761935e-10, 'dumplings': 2.7789358370000627e-08, 'edamame': 1.2257672654669705e-09, 'eggs_benedict': 1.7847326034825528e-06, 'escargots': 4.729781267087674e-06, 'falafel': 1.057799750014965e-07, 'filet_mignon': 9.7803103926708e-07, 'fish_and_chips': 2.411812971558902e-08, 'foie_gras': 1.980630059961186e-07, 'french_fries': 2.1170019903760107e-10, 'french_onion_soup': 8.037839620556042e-07, 'french_toast': 1.886677125639835e-08, 'fried_calamari': 3.5154143915860914e-06, 'fried_rice': 2.417278155419922e-09, 'frozen_yogurt': 1.3305869427104877e-11, 'garlic_bread': 5.037839514443476e-07, 'gnocchi': 6.530624523293227e-05, 'greek_salad': 3.964517958365832e-08, 'grilled_cheese_sandwich': 2.2158333194965962e-06, 'grilled_salmon': 2.8993943033128744e-06, 'guacamole': 3.015505356529502e-08, 'gyoza': 4.104923512215919e-09, 'hamburger': 2.708931690076355e-10, 'hot_and_sour_soup': 2.1728809862509024e-09, 'hot_dog': 7.266784951553973e-09, 'huevos_rancheros': 0.0001589559979038313, 'hummus': 0.00018752935284283012, 'ice_cream': 1.0000619776739939e-10, 'lasagna': 0.002402353333309293, 'lobster_bisque': 0.0007481773500330746, 'lobster_roll_sandwich': 9.019604476634413e-07, 'macaroni_and_cheese': 6.604105351470935e-08, 'macarons': 4.052758684203184e-10, 'miso_soup': 8.304305160189429e-11, 'mussels': 1.4905478451510135e-07, 'nachos': 1.0367559255541892e-08, 'omelette': 1.593216620676685e-05, 'onion_rings': 3.7136052566388855e-10, 'oysters': 1.944729710601223e-08, 'pad_thai': 5.135359693042574e-09, 'paella': 1.417079431575985e-07, 'pancakes': 4.537273525784258e-06, 'panna_cotta': 1.2441113694805495e-09, 'peking_duck': 3.686591254492555e-09, 'pho': 1.787771680028527e-06, 'pizza': 3.0199618777260184e-05, 'pork_chop': 1.9420731405261904e-05, 'poutine': 3.0199589495794044e-09, 'prime_rib': 2.6074596348735213e-07, 'pulled_pork_sandwich': 1.4259367162594572e-06, 'ramen': 1.2274898431030579e-08, 'ravioli': 0.0004831904952879995, 'red_velvet_cake': 1.2236777563456869e-11, 'risotto': 3.5618579197205236e-09, 'samosa': 1.1307191925880034e-05, 'sashimi': 3.3406628574539354e-08, 'scallops': 8.976988965514465e-07, 'seaweed_salad': 2.188233061417577e-08, 'shrimp_and_grits': 0.0009327345178462565, 'spaghetti_bolognese': 6.795530111958215e-07, 'spaghetti_carbonara': 3.18420489975324e-09, 'spring_rolls': 8.592571276722083e-08, 'steak': 5.759585519626853e-07, 'strawberry_shortcake': 1.7769200000294916e-10, 'sushi': 2.4618196370340684e-10, 'tacos': 6.091212298997561e-07, 'takoyaki': 4.500197992030053e-09, 'tiramisu': 1.3370865303841128e-08, 'tuna_tartare': 2.06905860977713e-06, 'waffles': 1.811772953885793e-10}\n",
      "Prediction time: 0.061 seconds\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "from PIL import Image\n",
    "\n",
    "# Get a list of all test image filepaths\n",
    "test_data_paths = list(Path(test_dir).glob(\"*/*.jpg\"))\n",
    "\n",
    "# Randomly select a test image path\n",
    "random_image_path = random.sample(test_data_paths, k=1)[0]\n",
    "\n",
    "# Open the target image\n",
    "image = Image.open(random_image_path)\n",
    "print(f\"[INFO] Predicting on image path: {random_image_path}\\n\")\n",
    "\n",
    "# Predict on the target image and print out the outputs\n",
    "pred_label_and_prob, pred_time = predict(img=image)\n",
    "\n",
    "print(f\"Prediction label and probability dictionary: \\n{pred_label_and_prob}\")\n",
    "print(f\"Prediction time: {pred_time} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dc963f90-b35b-4424-9bf0-1cf364ea0037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(pred_label_and_prob[\"caprese_salad\"] * 100, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "138fe739-8f32-4e55-91aa-7f639b4a8aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of example inputs to our Gradio demo\n",
    "examples_list = [[str(filepath)] for filepath in random.sample(test_data_paths, k=10)]\n",
    "len(examples_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "618bf909-aeb0-4628-ad5d-65db5ccc43ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.702,\n",
       " 'test_acc': 86.2,\n",
       " 'model_size (MB)': 30,\n",
       " 'time_per_pred_gpu': 0.027}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vit_b16_stats = {'test_loss': 1.014,\n",
    "                 'test_acc': 75.0,\n",
    "                 'model_size (MB)': 327, 'time_per_pred_gpu': 0.0209}\n",
    "effnetb4_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "44a14cd0-c923-4875-aa8e-d23874423fb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_acc</th>\n",
       "      <th>model_size (MB)</th>\n",
       "      <th>time_per_pred_gpu</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.702</td>\n",
       "      <td>86.2</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0270</td>\n",
       "      <td>EffNetB4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.014</td>\n",
       "      <td>75.0</td>\n",
       "      <td>327</td>\n",
       "      <td>0.0209</td>\n",
       "      <td>ViT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_loss  test_acc  model_size (MB)  time_per_pred_gpu     model\n",
       "0      0.702      86.2               30             0.0270  EffNetB4\n",
       "1      1.014      75.0              327             0.0209       ViT"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([effnetb4_stats, vit_b16_stats])\n",
    "df[\"model\"] = [\"EffNetB4\", \"ViT\"]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1a3848f7-f71b-4279-9bf6-e04f13f9bbcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Create a plot from model comparison DataFrame\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "scatter = ax.scatter(data=df, \n",
    "                     x=\"time_per_pred_gpu\", \n",
    "                     y=\"test_acc\", \n",
    "                     c=[\"green\", \"orange\"], # what colours to use?\n",
    "                     s=\"model_size (MB)\") # size the dots by the model sizes\n",
    "\n",
    "# 2. Add titles, labels and customize fontsize for aesthetics\n",
    "ax.set_title(\"FoodVision Inference Speed vs Performance\", fontsize=18)\n",
    "ax.set_xlabel(\"Prediction time per image (seconds)\", fontsize=14)\n",
    "ax.set_ylabel(\"Test accuracy (%)\", fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "ax.grid(True)\n",
    "\n",
    "# 3. Annotate with model names\n",
    "for index, row in df.iterrows():\n",
    "    ax.annotate(text=row[\"model\"],\n",
    "                xy=(row[\"time_per_pred_gpu\"]+0.0006, row[\"test_acc\"]+0.03),\n",
    "                size=12)\n",
    "\n",
    "# 4. Create a legend based on model sizes\n",
    "handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.5)\n",
    "model_size_legend = ax.legend(handles, \n",
    "                              labels, \n",
    "                              loc=\"lower right\", \n",
    "                              title=\"Model size (MB)\",\n",
    "                              fontsize=12)\n",
    "\n",
    "# Show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "329b99c7-0606-422a-bacf-c2d487ce2868",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}