{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# !git clone https://github.com/facebookresearch/CutLER.git\n",
    "\n",
    "import os\n",
    "from PIL import Image\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "class CustomImageDataset(Dataset):\n",
    "    def __init__(self, image_dir, transform=None):\n",
    "        self.image_dir = image_dir\n",
    "        self.image_files = os.listdir(image_dir)\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.image_files)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_path = os.path.join(self.image_dir, self.image_files[idx])\n",
    "        image = Image.open(img_path).convert(\"RGB\")\n",
    "        if self.transform:\n",
    "            image = self.transform(image)\n",
    "        return image\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((480, 480)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])\n",
    "dataset = CustomImageDataset(\"img\", transform=transform)\n",
    "dataloader = DataLoader(dataset, batch_size=16, shuffle=True)\n",
    "\n",
    "\n",
    "#perform prediction\n",
    "model.load_state_dict(torch.load(\"cutler_model_weights.pth\"))\n",
    "model.eval()  # Set the model to evaluation mode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!git clone --recursive https://github.com/facebookresearch/CutLER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cutLer",
   "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": "3.12.7"
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}