Instructions to use james-emi/darpa_ir-f8v2-person-mask2former-swinb-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use james-emi/darpa_ir-f8v2-person-mask2former-swinb-v4 with Transformers:
# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("james-emi/darpa_ir-f8v2-person-mask2former-swinb-v4") model = Mask2FormerForUniversalSegmentation.from_pretrained("james-emi/darpa_ir-f8v2-person-mask2former-swinb-v4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da9050926081dd368d6244169e357038531f957d6a632ef1d21f9e8727a7c6c7
- Size of remote file:
- 432 MB
- SHA256:
- 7bbd85a857aaf3abdaac7c918f623e648aba8bb8ac3906dd3da0b4837790a588
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