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# 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).
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