Instructions to use james-emi/ft-dtc-y1-person-idis1-swin_l-mask2former with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use james-emi/ft-dtc-y1-person-idis1-swin_l-mask2former with Transformers:
# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("james-emi/ft-dtc-y1-person-idis1-swin_l-mask2former") model = Mask2FormerForUniversalSegmentation.from_pretrained("james-emi/ft-dtc-y1-person-idis1-swin_l-mask2former") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1767655212f6bb91022812dd920c686ae34d8b5a4e7e9d360ab1dad25faddb57
- Size of remote file:
- 866 MB
- SHA256:
- 1fbe35ed14c342b1440f3f84ef8e28154366f337e4d1768af5dc8c7ebc872716
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