fixed readme

README.md

@@ -1,104 +1,13 @@
 ---
-license: apache-2.0
-tags:
-  - vision
-  - image-classification
-widget:
-  - src: >-
-      https://huggingface.co/jordandavis/yolo-human-parse/blob/main/sample_images/image_one.jpg
-    example_title: Straight ahead
-  - src: >-
-      Looking back
-    example_title: Teapot
-  - src: >-
-      https://huggingface.co/jordandavis/yolo-human-parse/blob/main/sample_images/image_three.jpg
-    example_title: Sweats
+title: YOLO Human Parse
+emoji: 🧑
+colorFrom: red
+colorTo: red
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
 ---
 
-
-# YOLO Segmentation Model for Human Body Parts and Objects
-
-This repository contains a fine-tuned YOLO (You Only Look Once) segmentation model designed to detect and segment various human body parts and objects in images.
-
-## Model Overview
-
-The model is based on the YOLO architecture and has been fine-tuned to detect and segment the following classes:
-
-0. Hair
-1. Face
-2. Neck
-3. Arm
-4. Hand
-5. Back
-6. Leg
-7. Foot
-8. Outfit
-9. Person
-10. Phone
-
-## Installation
-
-To use this model, you'll need to have the appropriate YOLO framework installed. Please follow these steps:
-
-1. Clone this repository:
-   ```
-   git clone https://github.com/your-username/yolo-segmentation-human-parts.git
-   cd yolo-segmentation-human-parts
-   ```
-
-2. Install the required dependencies:
-   ```
-   pip install -r requirements.txt
-   ```
-
-## Usage
-
-To use the model for inference, you can use the following Python script:
-
-```python
-from ultralytics import YOLO
-
-# Load the model
-model = YOLO('path/to/your/model.pt')
-
-# Perform inference on an image
-results = model('path/to/your/image.jpg')
-
-# Process the results
-for result in results:
-    boxes = result.boxes  # Bounding boxes
-    masks = result.masks  # Segmentation masks
-    # Further processing...
-```
-
-## Training
-
-If you want to further fine-tune the model on your own dataset, please follow these steps:
-
-1. Prepare your dataset in the YOLO format.
-2. Modify the `data.yaml` file to reflect your dataset structure and classes.
-3. Run the training script:
-   ```
-   python train.py --img 640 --batch 16 --epochs 100 --data data.yaml --weights yolov5s-seg.pt
-   ```
-
-## Evaluation
-
-To evaluate the model's performance on your test set, use:
-
-```
-python val.py --weights path/to/your/model.pt --data data.yaml --task segment
-```
-
-## Contributing
-
-Contributions to improve the model or extend its capabilities are welcome. Please submit a pull request or open an issue to discuss proposed changes.
-
-## License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
-## Acknowledgments
-
-- Thanks to the YOLO team for the original implementation.
-- Gratitude to all contributors who helped in fine-tuning and improving this model.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference