oneformer_ade20k_swin_large

Introduction

OneFormer is a universal image segmentation framework that requires training only once with a single architecture and dataset. It excels in semantic, instance, and panoptic segmentation tasks, outperforming specialized models. Introduced in the paper "OneFormer: One Transformer to Rule Universal Image Segmentation" by Jain et al., it employs a task token to dynamically guide tasks during training and inference.

Architecture

OneFormer leverages a Swin Transformer backbone and a task token that conditions the model on specific tasks. This structure allows it to operate effectively across different segmentation tasks using the same model and dataset. The architecture supports multi-tasking without needing separate models for each task, enhancing efficiency and performance.

Training

The model is trained on the ADE20k dataset, which covers a wide range of objects and scenes. Its universal architecture allows it to learn from this single dataset and apply its learning across various segmentation tasks. The use of task tokens enables efficient training by dynamically adjusting to the task requirements during learning.

Guide: Running Locally

To run the OneFormer model locally:

1. Install Required Libraries:

   • Ensure you have transformers and torch installed. You can install these using pip:

     pip install transformers torch
     

2. Load and Process an Image:

   • Use the code snippet provided to process an image for semantic, instance, and panoptic segmentation tasks. Replace the image URL with your own if needed.

3. Inference:

   • Load the model and processor using from_pretrained with the checkpoint "shi-labs/oneformer_ade20k_swin_large".
   • Perform segmentation tasks by preparing inputs using the processor and passing them to the model.

4. Post-Processing:

   • Use the processor's post-processing methods to obtain the final segmentation maps.

For optimal performance, consider using cloud GPUs such as those available on AWS, Google Cloud, or Azure, which provide the necessary computational power for running large models efficiently.

License

The OneFormer model is released under the MIT License, allowing for flexible use, modification, and distribution.