Panoptic Segmentation: Segment Everything, Everywhere, All At Once

Panoptic Segmentation is a breakthrough technology that has the ability to segment every object with semantics, cover every pixel in the image, and support all compositions of prompts at once. The paper and GitHub repository provide more information on this technology, including a segmentation interface built with a single pre-trained model.

The GitHub repository for this technology, available at, contains the demo code, pre-trained models, and dataset preparation scripts. It is recommended to download the demo code using the command:

git clone [email protected]:UX-Decoder/Segment-Everything-Everywhere-All-At-Once.git && cd Segment-Everything-Everywhere-All-At-Once/demo_code && sh

However, it is worth noting that the big GB files are not included in the repository, and should be downloaded separately.

The technology uses a single pre-trained model to generate panoptic segmentation outputs, and provides a graphical user interface for visualization and interaction with the results. The model architecture is based on the EfficientNet backbone and the Swin Transformer architecture, and is trained using a combination of self-supervised and supervised learning techniques.

The paper presents several impressive examples of the technology's capabilities, including segmenting complex scenes with multiple objects, handling occlusion and partial visibility, and generalizing to unseen categories. With its impressive capabilities, there is a lot of excitement and anticipation for what the future holds for Panoptic Segmentation and its potential applications, including in Stable Diffusion and other fields.

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