Instructions to use facebook/mask2former-swin-tiny-coco-panoptic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mask2former-swin-tiny-coco-panoptic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="facebook/mask2former-swin-tiny-coco-panoptic")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-coco-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-coco-panoptic", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/mask2former-swin-tiny-coco-panoptic: direct link, hf CLI and curl.
- Browser
- Download file 190 MB
-
https://huggingface.co/facebook/mask2former-swin-tiny-coco-panoptic/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/mask2former-swin-tiny-coco-panoptic/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/mask2former-swin-tiny-coco-panoptic/resolve/main/pytorch_model.bin
190 MB
- Xet hash:
- 88d1a844db2e95cde7a42a882797aa4aab1112c3068111e10ef3cabec5c3a47e
- Size of remote file:
- 190 MB
- SHA256:
- 38b05514f004bdc897fb479d4a2a6c4a1fdff0b2e3910e17783a0dd2abb2580a
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