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:

pip install transformers torch

Inference Example

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

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:

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

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.