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:
- e90d3984cd3f65e8e5c21a6fe0f0f259b4f42a15431d61f65040373eedd94a08
- Size of remote file:
- 5.78 kB
- SHA256:
- fa9b26a39e255099c063852f544a250019fd35fa2085d15df7d4faeaada94578
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