Instructions to use hf-internal-testing/tiny-random-VitPoseForPoseEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-VitPoseForPoseEstimation with Transformers:
# Load model directly from transformers import AutoImageProcessor, VitPoseForPoseEstimation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-VitPoseForPoseEstimation") model = VitPoseForPoseEstimation.from_pretrained("hf-internal-testing/tiny-random-VitPoseForPoseEstimation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edacce78ecdfcbd4d116f6fbd415a221cf5e60971ebdff39a82b53ae88fa4832
- Size of remote file:
- 5.06 MB
- SHA256:
- 69c042c5e396103a7d1d6ab0e326a2da9d1b1ee9815cb452d49188e8a2560769
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.