diff --git a/dogs_muffins.ipynb b/dogs_muffins.ipynb
new file mode 100644
index 0000000..f242c58
--- /dev/null
+++ b/dogs_muffins.ipynb
@@ -0,0 +1,550 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IVGsNc0MUtLr",
+        "outputId": "f2f21d98-02c8-44b8-f13e-2d85a1ddd2c9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
+            "Warning: Model file not found at /content/drive/MyDrive/Colab Notebooks/models/resnet_model_x50_175. Starting with a new model.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from torch import Tensor\n",
+        "from torch import nn\n",
+        "from torch import optim\n",
+        "from torchvision.datasets import ImageFolder\n",
+        "from torch.utils.data import Dataset, DataLoader, random_split\n",
+        "from PIL import Image\n",
+        "import torchvision as tv\n",
+        "from torchvision import models\n",
+        "import os\n",
+        "import numpy\n",
+        "from tqdm import tqdm\n",
+        "\n",
+        "import zipfile\n",
+        "from google.colab import drive\n",
+        "\n",
+        "drive.mount('/content/drive')\n",
+        "zipped_test = '/content/drive/MyDrive/для кодов/маффины/test.zip'\n",
+        "zipped_train ='/content/drive/MyDrive/для кодов/маффины/test.zip'\n",
+        "z = zipfile.ZipFile(zipped_test, 'r')\n",
+        "z.extractall(path='/content/dataset/test')\n",
+        "z = zipfile.ZipFile(zipped_train, 'r')\n",
+        "z.extractall(path='/content/dataset/train')\n",
+        "import gc\n",
+        "gc.collect()\n",
+        "\n",
+        "\n",
+        "DATASET_ROOT_DIR = '/content/dataset/'\n",
+        "BATCH_SIZE = 64\n",
+        "if torch.cuda.is_available():\n",
+        "  device = 'cuda'\n",
+        "else:\n",
+        "  device = 'cpu'\n",
+        "device\n",
+        "\n",
+        "\n",
+        "transforms = tv.transforms.Compose([\n",
+        "    tv.transforms.Resize((224, 224)),\n",
+        "    tv.transforms.ToTensor(),\n",
+        "    tv.transforms.RandomHorizontalFlip(0.5),\n",
+        "])\n",
+        "\n",
+        "def is_valid_file(path):\n",
+        "    \"\"\"Checks if a file is a valid image file and not a hidden macOS file.\"\"\"\n",
+        "    return not path.startswith('._') and os.path.splitext(path)[-1].lower() in ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp']\n",
+        "\n",
+        "dataset = ImageFolder(root=os.path.join(DATASET_ROOT_DIR, 'train'),\n",
+        "                      transform=transforms,\n",
+        "                      is_valid_file=is_valid_file\n",
+        "                      ) # 0 - dogs\n",
+        "\n",
+        "\n",
+        "train_size = int(0.8 * len(dataset))\n",
+        "test_size = len(dataset) - train_size\n",
+        "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
+        "\n",
+        "\n",
+        "train_data = DataLoader(\n",
+        "                  train_dataset,\n",
+        "                  batch_size=BATCH_SIZE,\n",
+        "                  shuffle=True,\n",
+        "                  )\n",
+        "test_data = DataLoader(\n",
+        "                  test_dataset,\n",
+        "                  batch_size=BATCH_SIZE,\n",
+        "                  shuffle=True,\n",
+        "                  )\n",
+        "class CustomResNet(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(CustomResNet, self).__init__()\n",
+        "        self.resnet = models.resnet18(pretrained=False)\n",
+        "\n",
+        "        self.ep = 0\n",
+        "\n",
+        "        num_ftrs = self.resnet.fc.in_features\n",
+        "        self.resnet.fc = nn.Linear(num_ftrs, 2)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.resnet(x)\n",
+        "\n",
+        "\n",
+        "\n",
+        "model = CustomResNet()\n",
+        "model.to(device)\n",
+        "\n",
+        "\n",
+        "actual_ep=175\n",
+        "model_path = f'/content/drive/MyDrive/Colab Notebooks/models/resnet_model_x50_{actual_ep}'\n",
+        "if os.path.exists(model_path):\n",
+        "    model = torch.load(model_path)\n",
+        "else:\n",
+        "    print(f\"Warning: Model file not found at {model_path}. Starting with a new model.\")\n",
+        "loss_func = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.Adam(model.parameters(), lr = 1e-4)\n",
+        "\n",
+        "\n",
+        "def save_model_to_drive(model, name):\n",
+        "  pth = os.path.join('/content/drive/MyDrive/Colab Notebooks/models', name)\n",
+        "  torch.save(model, pth)\n",
+        "\n",
+        "\n",
+        "def get_acc():\n",
+        "  correct = 0\n",
+        "  total = 0"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R2crhrvkxJ8v",
+        "outputId": "c51a415a-b3fb-4dfc-a368-21fbd8561bfc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SBdgo5D9CobC",
+        "outputId": "15991dbf-ade9-475f-8ce0-ae10618d2ccb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Warning: Model file not found at /content/drive/MyDrive/Colab Notebooks/models/resnet_model_x50_175. Starting with a new model.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 37/37 [05:00<00:00,  8.12s/it]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1, Loss: 0.004813789390027523\n",
+            "Final accuracy: 1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from tqdm import tqdm\n",
+        "from PIL import Image\n",
+        "import torch\n",
+        "from torch import nn, optim\n",
+        "from torchvision import models, transforms\n",
+        "from torch.utils.data import Dataset, DataLoader\n",
+        "\n",
+        "# Dataset class with filtering\n",
+        "class CustomDataset(Dataset):\n",
+        "    def __init__(self, root_dir, transform=None):\n",
+        "        self.root_dir = root_dir\n",
+        "        self.transform = transform\n",
+        "        self.image_paths = []\n",
+        "        self.labels = []\n",
+        "\n",
+        "        for root, _, files in os.walk(root_dir):\n",
+        "            if '__MACOSX' in root:\n",
+        "                continue\n",
+        "            for file in files:\n",
+        "                if file.lower().endswith(('.png', '.jpg', '.jpeg')):\n",
+        "                    self.image_paths.append(os.path.join(root, file))\n",
+        "                    label = 0 if 'class0' in os.path.basename(os.path.dirname(os.path.join(root, file))) else 1\n",
+        "                    self.labels.append(label)\n",
+        "\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.image_paths)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        img_path = self.image_paths[idx]\n",
+        "        label = self.labels[idx]\n",
+        "        try:\n",
+        "            image = Image.open(img_path).convert(\"RGB\")\n",
+        "        except Exception as e:\n",
+        "            print(f\"Error opening image {img_path}: {e}\")\n",
+        "            return None, None\n",
+        "\n",
+        "        if self.transform:\n",
+        "            image = self.transform(image)\n",
+        "\n",
+        "        return image, label\n",
+        "\n",
+        "\n",
+        "class CustomResNet(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(CustomResNet, self).__init__()\n",
+        "        self.resnet = models.resnet18(pretrained=False)\n",
+        "        self.ep = 0\n",
+        "        num_ftrs = self.resnet.fc.in_features\n",
+        "        self.resnet.fc = nn.Linear(num_ftrs, 2)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.resnet(x)\n",
+        "\n",
+        "\n",
+        "def save_model_to_drive(model, name):\n",
+        "    pth = os.path.join('/content/drive/MyDrive/Colab Notebooks/models', name)\n",
+        "    torch.save(model, pth)\n",
+        "\n",
+        "\n",
+        "def get_acc(data_loader, model, device):\n",
+        "    correct = 0\n",
+        "    total = 0\n",
+        "    model.eval()\n",
+        "    with torch.no_grad():\n",
+        "        for img, label in data_loader:\n",
+        "            img = img.to(device)\n",
+        "            label = label.to(device)\n",
+        "            out = model(img)\n",
+        "            _, predicted = torch.max(out.data, 1)\n",
+        "            total += label.size(0)\n",
+        "            correct += (predicted == label).sum().item()\n",
+        "\n",
+        "    return correct / total\n",
+        "\n",
+        "\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "\n",
+        "transform = transforms.Compose([\n",
+        "    transforms.Resize((224, 224)),\n",
+        "    transforms.ToTensor(),\n",
+        "])\n",
+        "\n",
+        "train_data = CustomDataset('/content/dataset/train', transform=transform)\n",
+        "test_data = CustomDataset('/content/dataset/test', transform=transform)\n",
+        "\n",
+        "train_loader = DataLoader(train_data, batch_size=32, shuffle=True)\n",
+        "test_loader = DataLoader(test_data, batch_size=32, shuffle=False)\n",
+        "\n",
+        "model = CustomResNet()\n",
+        "model.to(device)\n",
+        "\n",
+        "actual_ep = 175\n",
+        "model_path = f'/content/drive/MyDrive/Colab Notebooks/models/resnet_model_x50_{actual_ep}'\n",
+        "if os.path.exists(model_path):\n",
+        "    model = torch.load(model_path)\n",
+        "    print(f\"Model loaded from {model_path}\")\n",
+        "else:\n",
+        "    print(f\"Warning: Model file not found at {model_path}. Starting with a new model.\")\n",
+        "\n",
+        "loss_func = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.Adam(model.parameters(), lr=1e-4)\n",
+        "\n",
+        "EPOCHS = 1\n",
+        "\n",
+        "for i in range(EPOCHS):\n",
+        "    model.train()\n",
+        "    for img, label in tqdm(train_loader):\n",
+        "        optimizer.zero_grad()\n",
+        "        img = img.to(device)\n",
+        "        label = label.to(device)\n",
+        "\n",
+        "        out = model(img)\n",
+        "        loss = loss_func(out, label)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "    model.ep += 1\n",
+        "\n",
+        "    print(f'Epoch {model.ep}, Loss: {loss.item()}')\n",
+        "\n",
+        "    if model.ep % 5 == 0:\n",
+        "        acc = get_acc(test_loader, model, device)\n",
+        "        print(f'Accuracy: {acc}')\n",
+        "        save_model_to_drive(model, f'resnet_model_x50_{model.ep}')\n",
+        "        print('Model saved!')\n",
+        "\n",
+        "final_acc = get_acc(test_loader, model, device)\n",
+        "print(f'Final accuracy: {final_acc}')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BNOhOqCaR2b6",
+        "outputId": "77a40a53-e355-45f1-82f7-264300539a27"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using device: cpu\n",
+            "CustomResNet parameters: 11,177,538\n",
+            "ResNext50_32x4d parameters: 25,028,904\n",
+            "Files in /content/dataset/test: ['muffin', '__MACOSX', 'chihuahua']\n",
+            "All files in directory: ['muffin', '__MACOSX', 'chihuahua']\n",
+            "No valid image files found in directory: /content/dataset/test. Check the directory path and file extensions. Found: ['muffin', '__MACOSX', 'chihuahua']\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from tqdm import tqdm\n",
+        "from PIL import Image\n",
+        "import torch\n",
+        "from torch import nn, optim\n",
+        "from torchvision import models, transforms\n",
+        "from torch.utils.data import Dataset, DataLoader\n",
+        "import torchvision as tv\n",
+        "from datetime import datetime\n",
+        "\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "print(f\"Using device: {device}\")\n",
+        "\n",
+        "class CustomResNet(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(CustomResNet, self).__init__()\n",
+        "        self.resnet = models.resnet18(pretrained=False)\n",
+        "        num_ftrs = self.resnet.fc.in_features\n",
+        "        self.resnet.fc = nn.Linear(num_ftrs, 2)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.resnet(x)\n",
+        "\n",
+        "\n",
+        "class MyDataset(Dataset):\n",
+        "    def __init__(self, root, transforms=None):\n",
+        "        all_files = os.listdir(root)\n",
+        "        print(f\"All files in directory: {all_files}\")\n",
+        "\n",
+        "        self.images = [\n",
+        "            f for f in all_files\n",
+        "            if os.path.isfile(os.path.join(root, f)) and\n",
+        "            '__MACOSX' not in f and\n",
+        "            any(f.lower().endswith(ext) for ext in ('.png', '.jpg', '.jpeg', '.tif', '.tiff', '.dog', '.muf'))\n",
+        "        ]\n",
+        "\n",
+        "        if not self.images:\n",
+        "            raise ValueError(\n",
+        "                f\"No valid image files found in directory: {root}. \"\n",
+        "                f\"Check the directory path and file extensions. Found: {all_files}\"\n",
+        "            )\n",
+        "\n",
+        "        print(f\"Filtered images: {self.images}\")\n",
+        "\n",
+        "        self.root = root\n",
+        "        self.transforms = transforms\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.images)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        img_name = os.path.join(self.root, self.images[idx])\n",
+        "        image = Image.open(img_name).convert('RGB')\n",
+        "        if self.transforms:\n",
+        "            image = self.transforms(image)\n",
+        "        return image, img_name\n",
+        "\n",
+        "transforms_ = transforms.Compose([\n",
+        "    transforms.Resize((224, 224)),\n",
+        "    transforms.ToTensor(),\n",
+        "])\n",
+        "\n",
+        "model = CustomResNet().to(device)\n",
+        "\n",
+        "print(f'CustomResNet parameters: {sum(p.numel() for p in model.parameters()):,}')\n",
+        "resnext_model = models.resnext50_32x4d(pretrained=False)\n",
+        "print(f'ResNext50_32x4d parameters: {sum(p.numel() for p in resnext_model.parameters()):,}')\n",
+        "\n",
+        "test_dataset_path = '/content/dataset/test'\n",
+        "\n",
+        "if not os.path.exists(test_dataset_path):\n",
+        "    print(f\"The directory {test_dataset_path} does not exist.\")\n",
+        "else:\n",
+        "    files_in_dir = os.listdir(test_dataset_path)\n",
+        "    print(f\"Files in {test_dataset_path}: {files_in_dir}\")\n",
+        "\n",
+        "    try:\n",
+        "        d = MyDataset(test_dataset_path, transforms_)\n",
+        "\n",
+        "        dtldr = DataLoader(d, batch_size=1, shuffle=True)\n",
+        "\n",
+        "        for i, (img, img_name) in enumerate(dtldr):\n",
+        "            img = img.to(device)\n",
+        "            out = model(img)\n",
+        "            class_label = torch.argmax(out).item()\n",
+        "\n",
+        "            name = 'dog' if class_label == 0 else 'muf'\n",
+        "\n",
+        "            base, ext = os.path.splitext(img_name[0])\n",
+        "            new_name = os.path.join(test_dataset_path, f\"{name}_{i}{ext}\")\n",
+        "            os.rename(img_name[0], new_name)\n",
+        "            print(f\"Renamed {img_name[0]} to {new_name}\")\n",
+        "    except ValueError as e:\n",
+        "        print(e)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kSz2jJjSC_St"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from PIL import Image\n",
+        "import pandas as pd\n",
+        "import zipfile\n",
+        "\n",
+        "test_zip_path = '/content/drive/MyDrive/для кодов/маффины/test.zip'\n",
+        "new_extracted_folder = '/content/dataset/new_test/'\n",
+        "\n",
+        "\n",
+        "with zipfile.ZipFile(test_zip_path, 'r') as zip_ref:\n",
+        "    zip_ref.extractall(new_extracted_folder)\n",
+        "\n",
+        "extracted_folder = new_extracted_folder\n",
+        "\n",
+        "\n",
+        "predictions = []\n",
+        "\n",
+        "model.eval()\n",
+        "\n",
+        "for image_file in os.listdir(extracted_folder):\n",
+        "    image_path = os.path.join(extracted_folder, image_file)\n",
+        "\n",
+        "\n",
+        "    if os.path.isfile(image_path):\n",
+        "\n",
+        "        with Image.open(image_path) as img:\n",
+        "\n",
+        "            img = transforms(img).unsqueeze(0).to(device)\n",
+        "\n",
+        "\n",
+        "            with torch.no_grad():\n",
+        "                model.eval()\n",
+        "                output = model(img)\n",
+        "                _, predicted = torch.max(output, 1)\n",
+        "\n",
+        "\n",
+        "            predictions.append({'ID': image_file, 'Label': predicted.item()})\n",
+        "            #0=dogs  1=muffins\n",
+        "\n",
+        "\n",
+        "predictions_df = pd.DataFrame(predictions)\n",
+        "\n",
+        "\n",
+        "predictions_df.to_csv('/content/drive/MyDrive/Colab Notebooks/predictions.csv', index=False)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import csv\n",
+        "import os\n",
+        "from pathlib import Path\n",
+        "\n",
+        "output_csv_path = '/content/drive/My Drive/results.csv'\n",
+        "\n",
+        "results = [\"muffin\" if value else \"chihuahua\" for value in y_pred]\n",
+        "\n",
+        "labels = list(valid_files.iterdir())\n",
+        "\n",
+        "with open(output_csv_path, \"w\", newline=\"\") as f:\n",
+        "    fieldnames = [\"ID\", \"Label\"]\n",
+        "    writer = csv.DictWriter(f, fieldnames=fieldnames)\n",
+        "    writer.writeheader()\n",
+        "\n",
+        "    for label, result in zip(labels, results):\n",
+        "        writer.writerow({fieldnames[0]: label.name, fieldnames[1]: result})\n",
+        "\n",
+        "print(f\"Results saved to {output_csv_path}\")"
+      ],
+      "metadata": {
+        "id": "0YFwmp9K2ztj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2AQNnYfKcoSH"
+      },
+      "source": [
+        "# Новый раздел"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/dogs_muffins_modified.ipynb b/dogs_muffins_modified.ipynb
new file mode 100644
index 0000000..84a00ff
--- /dev/null
+++ b/dogs_muffins_modified.ipynb
@@ -0,0 +1,522 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IVGsNc0MUtLr",
+        "outputId": "f2f21d98-02c8-44b8-f13e-2d85a1ddd2c9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
+            "Warning: Model file not found at /content/drive/MyDrive/Colab Notebooks/models/resnet_model_x50_175. Starting with a new model.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from torch import Tensor\n",
+        "from torch import nn\n",
+        "from torch import optim\n",
+        "from torchvision.datasets import ImageFolder\n",
+        "from torch.utils.data import Dataset, DataLoader, random_split\n",
+        "from PIL import Image\n",
+        "import torchvision as tv\n",
+        "from torchvision import models\n",
+        "import os\n",
+        "import numpy\n",
+        "from tqdm import tqdm\n",
+        "\n",
+        "import zipfile\n",
+        "from google.colab import drive\n",
+        "\n",
+        "drive.mount('/content/drive')\n",
+        "zipped_test = '/content/drive/MyDrive/для кодов/маффины/test.zip'\n",
+        "zipped_train ='/content/drive/MyDrive/для кодов/маффины/test.zip'\n",
+        "z = zipfile.ZipFile(zipped_test, 'r')\n",
+        "z.extractall(path='/content/dataset/test')\n",
+        "z = zipfile.ZipFile(zipped_train, 'r')\n",
+        "z.extractall(path='/content/dataset/train')\n",
+        "import gc\n",
+        "gc.collect()\n",
+        "\n",
+        "\n",
+        "DATASET_ROOT_DIR = '/content/dataset/'\n",
+        "BATCH_SIZE = 64\n",
+        "if torch.cuda.is_available():\n",
+        "  device = 'cuda'\n",
+        "else:\n",
+        "  device = 'cpu'\n",
+        "device\n",
+        "\n",
+        "\n",
+        "transforms = tv.transforms.Compose([\n",
+        "    tv.transforms.Resize((224, 224)),\n",
+        "    tv.transforms.ToTensor(),\n",
+        "    tv.transforms.RandomHorizontalFlip(0.5),\n",
+        "])\n",
+        "\n",
+        "def is_valid_file(path):\n",
+        "    \"\"\"Checks if a file is a valid image file and not a hidden macOS file.\"\"\"\n",
+        "    return not path.startswith('._') and os.path.splitext(path)[-1].lower() in ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp']\n",
+        "\n",
+        "dataset = ImageFolder(root=os.path.join(DATASET_ROOT_DIR, 'train'),\n",
+        "                      transform=transforms,\n",
+        "                      is_valid_file=is_valid_file\n",
+        "                      ) # 0 - dogs\n",
+        "\n",
+        "\n",
+        "train_size = int(0.8 * len(dataset))\n",
+        "test_size = len(dataset) - train_size\n",
+        "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
+        "\n",
+        "\n",
+        "train_data = DataLoader(\n",
+        "                  train_dataset,\n",
+        "                  batch_size=BATCH_SIZE,\n",
+        "                  shuffle=True,\n",
+        "                  )\n",
+        "test_data = DataLoader(\n",
+        "                  test_dataset,\n",
+        "                  batch_size=BATCH_SIZE,\n",
+        "                  shuffle=True,\n",
+        "                  )\n",
+        "class CustomResNet(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(CustomResNet, self).__init__()\n",
+        "        self.resnet = models.resnet18(pretrained=False)\n",
+        "\n",
+        "        self.ep = 0\n",
+        "\n",
+        "        num_ftrs = self.resnet.fc.in_features\n",
+        "        self.resnet.fc = nn.Linear(num_ftrs, 2)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.resnet(x)\n",
+        "\n",
+        "\n",
+        "\n",
+        "model = CustomResNet()\n",
+        "model.to(device)\n",
+        "\n",
+        "\n",
+        "actual_ep=175\n",
+        "model_path = f'/content/drive/MyDrive/Colab Notebooks/models/resnet_model_x50_{actual_ep}'\n",
+        "if os.path.exists(model_path):\n",
+        "    model = torch.load(model_path)\n",
+        "else:\n",
+        "    print(f\"Warning: Model file not found at {model_path}. Starting with a new model.\")\n",
+        "loss_func = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.Adam(model.parameters(), lr = 1e-4)\n",
+        "\n",
+        "\n",
+        "def save_model_to_drive(model, name):\n",
+        "  pth = os.path.join('/content/drive/MyDrive/Colab Notebooks/models', name)\n",
+        "  torch.save(model, pth)\n",
+        "\n",
+        "\n",
+        "def get_acc():\n",
+        "  correct = 0\n",
+        "  total = 0"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R2crhrvkxJ8v",
+        "outputId": "c51a415a-b3fb-4dfc-a368-21fbd8561bfc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SBdgo5D9CobC",
+        "outputId": "15991dbf-ade9-475f-8ce0-ae10618d2ccb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Warning: Model file not found at /content/drive/MyDrive/Colab Notebooks/models/resnet_model_x50_175. Starting with a new model.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 37/37 [05:00<00:00,  8.12s/it]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1, Loss: 0.004813789390027523\n",
+            "Final accuracy: 1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "class CustomDataset(Dataset):\n",
+        "    def __init__(self, root_dir, transform=None):\n",
+        "        self.root_dir = root_dir\n",
+        "        self.transform = transform\n",
+        "        self.image_paths = []\n",
+        "        self.labels = []\n",
+        "\n",
+        "        for root, _, files in os.walk(root_dir):\n",
+        "            if '__MACOSX' in root:\n",
+        "                continue\n",
+        "            for file in files:\n",
+        "                if file.lower().endswith(('.png', '.jpg', '.jpeg')):\n",
+        "                    self.image_paths.append(os.path.join(root, file))\n",
+        "                    label = 0 if 'class0' in os.path.basename(os.path.dirname(os.path.join(root, file))) else 1\n",
+        "                    self.labels.append(label)\n",
+        "\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.image_paths)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        img_path = self.image_paths[idx]\n",
+        "        label = self.labels[idx]\n",
+        "        try:\n",
+        "            image = Image.open(img_path).convert(\"RGB\")\n",
+        "        except Exception as e:\n",
+        "            print(f\"Error opening image {img_path}: {e}\")\n",
+        "            return None, None\n",
+        "\n",
+        "        if self.transform:\n",
+        "            image = self.transform(image)\n",
+        "\n",
+        "        return image, label\n",
+        "\n",
+        "\n",
+        "class CustomResNet(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(CustomResNet, self).__init__()\n",
+        "        self.resnet = models.resnet18(pretrained=False)\n",
+        "        self.ep = 0\n",
+        "        num_ftrs = self.resnet.fc.in_features\n",
+        "        self.resnet.fc = nn.Linear(num_ftrs, 2)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.resnet(x)\n",
+        "\n",
+        "\n",
+        "def save_model_to_drive(model, name):\n",
+        "    pth = os.path.join('/content/drive/MyDrive/Colab Notebooks/models', name)\n",
+        "    torch.save(model, pth)\n",
+        "\n",
+        "\n",
+        "def get_acc(data_loader, model, device):\n",
+        "    correct = 0\n",
+        "    total = 0\n",
+        "    model.eval()\n",
+        "    with torch.no_grad():\n",
+        "        for img, label in data_loader:\n",
+        "            img = img.to(device)\n",
+        "            label = label.to(device)\n",
+        "            out = model(img)\n",
+        "            _, predicted = torch.max(out.data, 1)\n",
+        "            total += label.size(0)\n",
+        "            correct += (predicted == label).sum().item()\n",
+        "\n",
+        "    return correct / total\n",
+        "\n",
+        "\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "\n",
+        "transform = transforms.Compose([\n",
+        "    transforms.Resize((224, 224)),\n",
+        "    transforms.ToTensor(),\n",
+        "])\n",
+        "\n",
+        "train_data = CustomDataset('/content/dataset/train', transform=transform)\n",
+        "test_data = CustomDataset('/content/dataset/test', transform=transform)\n",
+        "\n",
+        "train_loader = DataLoader(train_data, batch_size=32, shuffle=True)\n",
+        "test_loader = DataLoader(test_data, batch_size=32, shuffle=False)\n",
+        "\n",
+        "model = CustomResNet()\n",
+        "model.to(device)\n",
+        "\n",
+        "actual_ep = 175\n",
+        "model_path = f'/content/drive/MyDrive/Colab Notebooks/models/resnet_model_x50_{actual_ep}'\n",
+        "if os.path.exists(model_path):\n",
+        "    model = torch.load(model_path)\n",
+        "    print(f\"Model loaded from {model_path}\")\n",
+        "else:\n",
+        "    print(f\"Warning: Model file not found at {model_path}. Starting with a new model.\")\n",
+        "\n",
+        "loss_func = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.Adam(model.parameters(), lr=1e-4)\n",
+        "\n",
+        "EPOCHS = 1\n",
+        "\n",
+        "for i in range(EPOCHS):\n",
+        "    model.train()\n",
+        "    for img, label in tqdm(train_loader):\n",
+        "        optimizer.zero_grad()\n",
+        "        img = img.to(device)\n",
+        "        label = label.to(device)\n",
+        "\n",
+        "        out = model(img)\n",
+        "        loss = loss_func(out, label)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "    model.ep += 1\n",
+        "\n",
+        "    print(f'Epoch {model.ep}, Loss: {loss.item()}')\n",
+        "\n",
+        "    if model.ep % 5 == 0:\n",
+        "        acc = get_acc(test_loader, model, device)\n",
+        "        print(f'Accuracy: {acc}')\n",
+        "        save_model_to_drive(model, f'resnet_model_x50_{model.ep}')\n",
+        "        print('Model saved!')\n",
+        "\n",
+        "final_acc = get_acc(test_loader, model, device)\n",
+        "print(f'Final accuracy: {final_acc}')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BNOhOqCaR2b6",
+        "outputId": "77a40a53-e355-45f1-82f7-264300539a27"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using device: cpu\n",
+            "CustomResNet parameters: 11,177,538\n",
+            "ResNext50_32x4d parameters: 25,028,904\n",
+            "Files in /content/dataset/test: ['muffin', '__MACOSX', 'chihuahua']\n",
+            "All files in directory: ['muffin', '__MACOSX', 'chihuahua']\n",
+            "No valid image files found in directory: /content/dataset/test. Check the directory path and file extensions. Found: ['muffin', '__MACOSX', 'chihuahua']\n"
+          ]
+        }
+      ],
+      "source": [
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "print(f\"Using device: {device}\")\n",
+        "\n",
+        "class CustomResNet(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(CustomResNet, self).__init__()\n",
+        "        self.resnet = models.resnet18(pretrained=False)\n",
+        "        num_ftrs = self.resnet.fc.in_features\n",
+        "        self.resnet.fc = nn.Linear(num_ftrs, 2)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.resnet(x)\n",
+        "\n",
+        "\n",
+        "class MyDataset(Dataset):\n",
+        "    def __init__(self, root, transforms=None):\n",
+        "        all_files = os.listdir(root)\n",
+        "        print(f\"All files in directory: {all_files}\")\n",
+        "\n",
+        "        self.images = [\n",
+        "            f for f in all_files\n",
+        "            if os.path.isfile(os.path.join(root, f)) and\n",
+        "            '__MACOSX' not in f and\n",
+        "            any(f.lower().endswith(ext) for ext in ('.png', '.jpg', '.jpeg', '.tif', '.tiff', '.dog', '.muf'))\n",
+        "        ]\n",
+        "\n",
+        "        if not self.images:\n",
+        "            raise ValueError(\n",
+        "                f\"No valid image files found in directory: {root}. \"\n",
+        "                f\"Check the directory path and file extensions. Found: {all_files}\"\n",
+        "            )\n",
+        "\n",
+        "        print(f\"Filtered images: {self.images}\")\n",
+        "\n",
+        "        self.root = root\n",
+        "        self.transforms = transforms\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.images)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        img_name = os.path.join(self.root, self.images[idx])\n",
+        "        image = Image.open(img_name).convert('RGB')\n",
+        "        if self.transforms:\n",
+        "            image = self.transforms(image)\n",
+        "        return image, img_name\n",
+        "\n",
+        "transforms_ = transforms.Compose([\n",
+        "    transforms.Resize((224, 224)),\n",
+        "    transforms.ToTensor(),\n",
+        "])\n",
+        "\n",
+        "model = CustomResNet().to(device)\n",
+        "\n",
+        "print(f'CustomResNet parameters: {sum(p.numel() for p in model.parameters()):,}')\n",
+        "resnext_model = models.resnext50_32x4d(pretrained=False)\n",
+        "print(f'ResNext50_32x4d parameters: {sum(p.numel() for p in resnext_model.parameters()):,}')\n",
+        "\n",
+        "test_dataset_path = '/content/dataset/test'\n",
+        "\n",
+        "if not os.path.exists(test_dataset_path):\n",
+        "    print(f\"The directory {test_dataset_path} does not exist.\")\n",
+        "else:\n",
+        "    files_in_dir = os.listdir(test_dataset_path)\n",
+        "    print(f\"Files in {test_dataset_path}: {files_in_dir}\")\n",
+        "\n",
+        "    try:\n",
+        "        d = MyDataset(test_dataset_path, transforms_)\n",
+        "\n",
+        "        dtldr = DataLoader(d, batch_size=1, shuffle=True)\n",
+        "\n",
+        "        for i, (img, img_name) in enumerate(dtldr):\n",
+        "            img = img.to(device)\n",
+        "            out = model(img)\n",
+        "            class_label = torch.argmax(out).item()\n",
+        "\n",
+        "            name = 'dog' if class_label == 0 else 'muf'\n",
+        "\n",
+        "            base, ext = os.path.splitext(img_name[0])\n",
+        "            new_name = os.path.join(test_dataset_path, f\"{name}_{i}{ext}\")\n",
+        "            os.rename(img_name[0], new_name)\n",
+        "            print(f\"Renamed {img_name[0]} to {new_name}\")\n",
+        "    except ValueError as e:\n",
+        "        print(e)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kSz2jJjSC_St"
+      },
+      "outputs": [],
+      "source": [
+        "test_zip_path = '/content/drive/MyDrive/для кодов/маффины/test.zip'\n",
+        "new_extracted_folder = '/content/dataset/new_test/'\n",
+        "\n",
+        "\n",
+        "with zipfile.ZipFile(test_zip_path, 'r') as zip_ref:\n",
+        "    zip_ref.extractall(new_extracted_folder)\n",
+        "\n",
+        "extracted_folder = new_extracted_folder\n",
+        "\n",
+        "\n",
+        "predictions = []\n",
+        "\n",
+        "model.eval()\n",
+        "\n",
+        "for image_file in os.listdir(extracted_folder):\n",
+        "    image_path = os.path.join(extracted_folder, image_file)\n",
+        "\n",
+        "\n",
+        "    if os.path.isfile(image_path):\n",
+        "\n",
+        "        with Image.open(image_path) as img:\n",
+        "\n",
+        "            img = transforms(img).unsqueeze(0).to(device)\n",
+        "\n",
+        "\n",
+        "            with torch.no_grad():\n",
+        "                model.eval()\n",
+        "                output = model(img)\n",
+        "                _, predicted = torch.max(output, 1)\n",
+        "\n",
+        "\n",
+        "            predictions.append({'ID': image_file, 'Label': predicted.item()})\n",
+        "            #0=dogs  1=muffins\n",
+        "\n",
+        "\n",
+        "predictions_df = pd.DataFrame(predictions)\n",
+        "\n",
+        "\n",
+        "predictions_df.to_csv('/content/drive/MyDrive/Colab Notebooks/predictions.csv', index=False)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "output_csv_path = '/content/drive/My Drive/results.csv'\n",
+        "\n",
+        "results = [\"muffin\" if value else \"chihuahua\" for value in y_pred]\n",
+        "\n",
+        "labels = list(valid_files.iterdir())\n",
+        "\n",
+        "with open(output_csv_path, \"w\", newline=\"\") as f:\n",
+        "    fieldnames = [\"ID\", \"Label\"]\n",
+        "    writer = csv.DictWriter(f, fieldnames=fieldnames)\n",
+        "    writer.writeheader()\n",
+        "\n",
+        "    for label, result in zip(labels, results):\n",
+        "        writer.writerow({fieldnames[0]: label.name, fieldnames[1]: result})\n",
+        "\n",
+        "print(f\"Results saved to {output_csv_path}\")"
+      ],
+      "metadata": {
+        "id": "0YFwmp9K2ztj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2AQNnYfKcoSH"
+      },
+      "source": [
+        "# Новый раздел"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/results.csv b/results.csv
new file mode 100644
index 0000000..c907bb1
--- /dev/null
+++ b/results.csv
@@ -0,0 +1,1184 @@
+ID,label
+224ec6c8-2c36-42b2-8741-467dd296f2bf.jpg,muffin
+1fb78620-9f25-4e66-a08c-135efac15a1b.jpg,muffin
+25152cee-b243-4d9c-b386-307eccc8c2bd.jpg,muffin
+21be8209-4e2a-4b77-afc2-b87d005227fa.jpg,muffin
+1f7a9345-ae53-4283-ad09-871f4c559949.jpg,chihuahua
+2230d476-b46d-414c-a41c-4de2a44d58ff.jpg,chihuahua
+28930da6-4208-4ad5-b677-effc298a772d.jpg,muffin
+226e204a-ef21-4daa-ae4f-d434cacc5533.jpg,muffin
+2533b6ac-064d-40ca-8070-6f9687edc768.jpg,chihuahua
+2cfedd9b-21ce-4bf8-a549-f74ba2699f25.jpg,chihuahua
+33eccaae-b4fc-4562-a9ea-18df7b9a6c0a.jpg,muffin
+2b338e37-50b6-4157-af28-21cf48c650f8.jpg,muffin
+32293456-e0e2-46d8-aa06-8b8f78da9336.jpg,chihuahua
+2dcdbb73-2fe5-4b9b-9b0e-3f6853877633.jpg,chihuahua
+2a706783-15a2-45c9-bb4a-59f572038084.jpg,chihuahua
+324a682b-3f71-4ec4-836e-7732e9e42e0a.jpg,muffin
+29df414d-d59a-4e37-85d7-ae8da92cc5a0.jpg,chihuahua
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