06f1fd69a6
4.5 KiB
This model was published in HF papers on 2021-12-18 and contributed 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 asinput_ids) or an image (provided to the model asconditional_pixel_values). One can also provide custom conditional embeddings (provided to the model asconditional_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 - call
CLIPSegModel
autodoc CLIPSegModel - forward - get_text_features - get_image_features
CLIPSegTextModel
autodoc CLIPSegTextModel - forward
CLIPSegVisionModel
autodoc CLIPSegVisionModel - forward
CLIPSegForImageSegmentation
autodoc CLIPSegForImageSegmentation - forward