| # 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). | |