Instructions to use facebook/maskformer-swin-base-ade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/maskformer-swin-base-ade with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="facebook/maskformer-swin-base-ade")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-ade") model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/maskformer-swin-base-ade: direct link, hf CLI and curl.
- Browser
- Download file 412 MB
-
https://huggingface.co/facebook/maskformer-swin-base-ade/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/maskformer-swin-base-ade/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/maskformer-swin-base-ade/resolve/main/pytorch_model.bin
412 MB
- Xet hash:
- 024bef7f9283bdf22ac1363b69c513edf14ad554d1b895a182aee6376e10e9e8
- Size of remote file:
- 412 MB
- SHA256:
- 03177538131eb2c906bbb9081b3ccc471cc52dc73db7332fb36b1698bcf6daca
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