Image Feature Extraction
LiteRT
LiteRT
PerceptionEncoder
on-device
android
gpu
clip
image-encoder
vit
rope
Instructions to use mlboydaisuke/PE-Core-base-patch16-224-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use mlboydaisuke/PE-Core-base-patch16-224-LiteRT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- PerceptionEncoder
How to use mlboydaisuke/PE-Core-base-patch16-224-LiteRT with PerceptionEncoder:
# Use PE-Core models as CLIP models import core.vision_encoder.pe as pe model = pe.CLIP.from_config("mlboydaisuke/PE-Core-base-patch16-224-LiteRT", pretrained=True)# Use any PE model as a vision encoder import core.vision_encoder.pe as pe model = pe.VisionTransformer.from_config("mlboydaisuke/PE-Core-base-patch16-224-LiteRT", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98258155d837bd3d7a969a82fe972254090bb918f4788eb9c0032db0fad1ee12
- Size of remote file:
- 187 MB
- SHA256:
- cd65fd1018afa40ce40a6107a07c69dc221e27dec546b97507bdd937900d6a7e
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