Instructions to use facebook/pe_core_gigantic_patch14_448_timm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PerceptionEncoder
How to use facebook/pe_core_gigantic_patch14_448_timm with PerceptionEncoder:
# Use any PE model as a vision encoder import core.vision_encoder.pe as pe model = pe.VisionTransformer.from_config("facebook/pe_core_gigantic_patch14_448_timm", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5faf50f958a269eb12bef43be3fe13cc2b3d76c0d21e2cde31c05e73b16326c4
- Size of remote file:
- 9.68 GB
- SHA256:
- a2d32bc0227d06b8ed95f2bb44d0078f469d7e1461213e1499a9bc17da665659
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.