CLIPSeg

This model was released on 2021-12-18 and added to Hugging Face Transformers on 2022-11-08.

CLIPSeg

Overview

The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero-shot and one-shot image segmentation.

The abstract from the paper is the following:

Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system that can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text or an image. This approach enables us to create a unified model (trained once) for three common segmentation tasks, which come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation. We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense prediction. After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail. This novel hybrid input allows for dynamic adaptation not only to the three segmentation tasks mentioned above, but to any binary segmentation task where a text or image query can be formulated. Finally, we find our system to adapt well to generalized queries involving affordances or properties

CLIPSeg overview. Taken from the original paper.

This model was contributed by nielsr. The original code can be found here.

Usage tips

• CLIPSegForImageSegmentation adds a decoder on top of CLIPSegModel. The latter is identical to CLIPModel.
• CLIPSegForImageSegmentation can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text (provided to the model as input_ids) or an image (provided to the model as conditional_pixel_values). One can also provide custom conditional embeddings (provided to the model as conditional_embeddings).

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIPSeg. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

• A notebook that illustrates zero-shot image segmentation with CLIPSeg.

CLIPSegConfig

[[autodoc]] CLIPSegConfig

CLIPSegTextConfig

[[autodoc]] CLIPSegTextConfig

CLIPSegVisionConfig

[[autodoc]] CLIPSegVisionConfig

CLIPSegProcessor

[[autodoc]] CLIPSegProcessor

CLIPSegModel

[[autodoc]] CLIPSegModel - forward - get_text_features - get_image_features

CLIPSegTextModel

[[autodoc]] CLIPSegTextModel - forward

CLIPSegVisionModel

[[autodoc]] CLIPSegVisionModel - forward

CLIPSegForImageSegmentation

[[autodoc]] CLIPSegForImageSegmentation - forward