Instructions to use CIDAS/clipseg-rd16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIDAS/clipseg-rd16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="CIDAS/clipseg-rd16")# Load model directly from transformers import AutoProcessor, CLIPSegForImageSegmentation processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd16") model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
tags:
- vision
- image-segmentation
inference: false
---
# CLIPSeg model
CLIPSeg model with reduce dimension 16. 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). |