Instructions to use facebook/mask2former-swin-small-coco-instance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mask2former-swin-small-coco-instance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="facebook/mask2former-swin-small-coco-instance")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-coco-instance") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-small-coco-instance", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/mask2former-swin-small-coco-instance: direct link, hf CLI and curl.
- Browser
- Download file 276 MB
-
https://huggingface.co/facebook/mask2former-swin-small-coco-instance/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/mask2former-swin-small-coco-instance/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/mask2former-swin-small-coco-instance/resolve/main/pytorch_model.bin
276 MB
- Xet hash:
- 4c24c249765d72eb276feb0d4e53259d4cf0af0c8f422d7c043ea8058c2227e7
- Size of remote file:
- 276 MB
- SHA256:
- 8585f734ad501cae71afff4515b0a43acae8362fcc5de184873caf19b4a438d9
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