Instructions to use adirik/maskformer-swin-base-sceneparse-instance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adirik/maskformer-swin-base-sceneparse-instance with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation processor = AutoImageProcessor.from_pretrained("adirik/maskformer-swin-base-sceneparse-instance") model = MaskFormerForInstanceSegmentation.from_pretrained("adirik/maskformer-swin-base-sceneparse-instance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from adirik/maskformer-swin-base-sceneparse-instance: direct link, hf CLI and curl.
- Browser
- Download file 411 MB
-
https://huggingface.co/adirik/maskformer-swin-base-sceneparse-instance/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://adirik/maskformer-swin-base-sceneparse-instance/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/adirik/maskformer-swin-base-sceneparse-instance/resolve/main/pytorch_model.bin
411 MB
- Xet hash:
- e1eb130bd0b70821aac1997e7e96c0845640dcc6b9798eccba4491493719c873
- Size of remote file:
- 411 MB
- SHA256:
- 9023f65e78dc7d3c3590147d8c09b407b3a8858f5ade4f239769bd432ea9d517
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