I S Net_ D I S

Introduction

The IS-NET_DIS model is designed for highly accurate dichotomous image segmentation, focusing on tasks such as background removal and computer vision. It uses intermediate supervision with both feature-level and mask-level guidance, outperforming various cutting-edge baselines.

Architecture

The model introduces a simple intermediate supervision baseline (IS-Net) that leverages feature-level and mask-level guidance for training. This structure allows it to act as a general self-learned supervision network, facilitating future research in dichotomous image segmentation.

Training

IS-NET_DIS is trained using the proposed DIS5K dataset. The model utilizes intermediate supervision without relying on additional tricks, which allows it to achieve superior performance compared to other baselines.

Guide: Running Locally

1. Setup Environment: Ensure you have PyTorch installed, as the model is built on this library.
2. Clone Repository: Clone the official code repository from GitHub.
3. Download Model Weights: Obtain the pre-trained model weights as specified in the repository.
4. Run Inference: Follow the instructions in the repository to perform image segmentation tasks.
5. Cloud GPUs: For efficient training and inference, consider using cloud GPUs offered by providers like AWS, GCP, or Azure.

License

The IS-NET_DIS model is released under the Apache 2.0 license, allowing for both personal and commercial use with proper attribution.