# CLIPSeg ## Overview The CLIPSeg model was proposed in [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen [CLIP](clip) model for zero- 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* 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`). CLIPSeg overview. Taken from the original paper. This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/timojl/clipseg). ## 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](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/CLIPSeg/Zero_shot_image_segmentation_with_CLIPSeg.ipynb). ## CLIPSegConfig [[autodoc]] CLIPSegConfig - from_text_vision_configs ## 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