Instructions to use CIDAS/clipseg-rd64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIDAS/clipseg-rd64 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="CIDAS/clipseg-rd64")# Load model directly from transformers import AutoProcessor, CLIPSegForImageSegmentation processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64") model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - vision | |
| - image-segmentation | |
| inference: false | |
| # CLIPSeg model | |
| CLIPSeg model with reduce dimension 64. It was introduced in the paper [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Lüddecke et al. and first released in [this repository](https://github.com/timojl/clipseg). | |
| # Intended use cases | |
| This model is intended for zero-shot and one-shot image segmentation. | |
| # Usage | |
| Refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/clipseg). |