Instructions to use facebook/PE-Spatial-B16-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PerceptionEncoder
How to use facebook/PE-Spatial-B16-512 with PerceptionEncoder:
# Use any PE model as a vision encoder import core.vision_encoder.pe as pe model = pe.VisionTransformer.from_config("facebook/PE-Spatial-B16-512", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download PE-Spatial-B16-512.pt from facebook/PE-Spatial-B16-512: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/facebook/PE-Spatial-B16-512/resolve/main/PE-Spatial-B16-512.pt
- Command line
-
hf download hf://facebook/PE-Spatial-B16-512/PE-Spatial-B16-512.pt
-
curl -L -o PE-Spatial-B16-512.pt https://huggingface.co/facebook/PE-Spatial-B16-512/resolve/main/PE-Spatial-B16-512.pt
346 MB
- Xet hash:
- 426993d322f3e3a5eb0485fe4111d4bc6523bd8b9a8dfabd4e57c663f643d588
- Size of remote file:
- 346 MB
- SHA256:
- 86217607f0bb28c0adb5ac3f9b0608ae22f6fb634bf1c16b2316847e8148a2a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.