# Segmentation Model

This repository hosts a segmentation model for image processing tasks. The model is designed to predict masks for images and highlight segmented areas based on a thresholded binary mask.

## Model Overview

This model is built to perform segmentation, particularly for tasks that involve identifying specific regions or objects in an image. The model takes an input image and produces a binary mask where the segmented areas are highlighted.

## How to Use

You can use this model directly with the Hugging Face `transformers` library.

### Installation

First, ensure you have the required libraries installed:

```bash
pip install transformers torch
```

### Inference Example

You can load the model and use it for inference with the following code:

```python
from transformers import AutoModel
import torch
import numpy as np
import matplotlib.pyplot as plt

# Load the model
model = AutoModel.from_pretrained("your-username/segmentation-model")

# Example: Load your input image (ensure it is preprocessed accordingly)
# image = load_image("path_to_your_image")

# Run the model on the image
outputs = model(image)

# Apply threshold to create binary mask
THRESHOLD = 0.1
binary_mask = (outputs.squeeze() > THRESHOLD).astype(np.uint8)

# Visualize the results
plt.imshow(binary_mask, cmap="gray")
plt.title("Predicted Binary Mask")
plt.show()
```

### Inputs

- **Images**: The input should be an image that matches the model's expected dimensions. Typically, these are images processed and resized appropriately.

### Outputs

- **Binary Mask**: The model returns a binary mask highlighting the segmented areas.

## Training Data

This model was trained on a dataset of images with corresponding masks. The dataset was preprocessed to include normalized pixel values and resized to fit the model’s input requirements.

## Evaluation

The model performance is evaluated based on the following metrics:
- **Intersection over Union (IoU)**
- **Dice Coefficient**
- **Pixel Accuracy**

## Fine-tuning

To fine-tune this model on your own dataset, follow these steps:

1. Prepare a dataset of images and masks.
2. Preprocess the images (normalization, resizing) and masks.
3. Fine-tune using Hugging Face’s `Trainer` class:

```python
from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=16,
    num_train_epochs=3,
    save_steps=10_000,
    save_total_limit=2,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()
```

## Model Details

- **Model Name**: Segmentation Model
- **Architecture**: [Add the model architecture used, e.g., U-Net, DeepLabv3]
- **Framework**: PyTorch

## Citation

If you use this model in your work, please cite:

```
@misc{sethanimesh,
  title={Segmentation Model},
  author={Animesh Seth},
  year={2024},
  howpublished={https://huggingface.co/sethanimesh/segmentation-model},
}
```

## License

This model is licensed under the [MIT License](LICENSE).