Instructions to use facebook/maskformer-swin-base-coco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/maskformer-swin-base-coco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="facebook/maskformer-swin-base-coco")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-coco") model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-coco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/maskformer-swin-base-coco: direct link, hf CLI and curl.
- Browser
- Download file 412 MB
-
https://huggingface.co/facebook/maskformer-swin-base-coco/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/maskformer-swin-base-coco/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/maskformer-swin-base-coco/resolve/main/pytorch_model.bin
412 MB
- Xet hash:
- 11f47bbb270e84fd24df15e9240410b357edea3d745d0c7163a2ab7e5c3e65a7
- Size of remote file:
- 412 MB
- SHA256:
- 8dd11d3789a3fd1c8ed02a3673e40b3ba9f4187a22f49d8b7a455eec532cb89c
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