Update README.md

README.md

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-# Clothes Segmentation
-
-![Sample Images and Segmentation Masks from Dataset](assets/dataset_examples.png)
-
-This project provides a solution for segmenting clothes into 18 categories using DINO, ViT and UNet models.
-
-* DINO: Pretrained backbone with a segmentation head
-  * https://arxiv.org/abs/2104.14294
-  * https://huggingface.co/facebook/dinov2-small
-* ViT: Pretrained vision transformer with a segmentation head
-  * https://arxiv.org/abs/2010.11929
-  * https://huggingface.co/google/vit-base-patch16-224
-* UNet: Custom implementation
-  * https://arxiv.org/abs/1505.04597
-
-Gradio is used for building a web interface and Weights & Biases for experiments tracking.
-
-## Installation
-
-1. Clone the repository:
-    ```bash
-    git clone https://github.com/your-project/clothes-segmentation.git
-    cd plant-classifier
-    ```
-
-2. Create and activate a virtual environment:
-    ```bash
-    python -m venv venv
-    source venv/bin/activate
-    ```
-
-3. Install dependencies:
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-## Usage
-
-### Training the Model
-   To train a model, specify one of the following using the --model argument: **dino**, **vit** or **unet**.
-   ```bash
-   python src/train.py --model dino
-   python src/train.py --model vit
-   python src/train.py --model unet
-   ```
-
-   You can also adjust other parameters, such as the number of epochs, batch size, and learning rate, by adding additional arguments. For example:
-   ```bash
-   python src/train.py --model unet --num-epochs 20 --batch-size 16 --learning-rate 0.001
-   ```
-
-### Launching the Gradio Interface
-   ```bash
-   python app.py
-   ```
-
-   Once the interface is running, you can select a model, upload an image and view the segmentation mask.
-   
-   ![Web Interface Screen](assets/spaces_screen.jpg)
-
-   #### добавить ссылку
-
-## Results
-
-| Model      | Test Micro Recall | Test Micro Precision | Test Macro Precision | Test Macro Recall | Test Accuracy | Test Loss | Train Micro Recall | Train Micro Precision | Train Macro Precision | Train Macro Recall | Train Accuracy | Train Loss |
-|------------|-------------------|----------------------|----------------------|-------------------|---------------|-----------|--------------------|-----------------------|-----------------------|--------------------|----------------|------------|
-| DINO       | 0.94986          | 0.94986             | 0.71364             | 0.67052          | 0.94986       | 0.53124   | 0.97019            | 0.97019              | 0.78185              | 0.72336           | 0.97019        | 0.30441    |
-| ViT        | 0.9358           | 0.9358              | 0.63939             | 0.58365          | 0.9358        | 0.71193   | 0.96734            | 0.96734              | 0.74418              | 0.66295           | 0.96734        | 0.31166    |
-| UNet       | 0.95798          | 0.95798             | 0.76354             | 0.7289           | 0.95798       | 0.56764   | 0.98035            | 0.98035              | 0.82934              | 0.82688           | 0.98035        | 0.25301    |
-
-### Training Results of DINO
-
-![DINO_test](assets/dino_test_plots.png)
-
-![DINO_train](assets/dino_train_plots.png)
-
-### Training Results of ViT
-
-![ViT_test](assets/vit_test_plots.png)
-
-![ViT_train](assets/vit_train_plots.png)
-
-### Training Results of UNet
-
-![UNet_test](assets/unet_test_plots.png)
-
-![UNet_train](assets/unet_train_plots.png)
+
+---
+title: Clothes Segmentation
+emoji: 🏆
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.12.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference