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
Card: base_model_relation: quantized (so the conversion lists under the base model's Quantizations, not Finetunes)
Browse files
README.md
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# Perception Encoder (PE-Core-B16-224) — LiteRT (TFLite) GPU
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- image-encoder
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- vit
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- rope
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base_model_relation: quantized
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# Perception Encoder (PE-Core-B16-224) — LiteRT (TFLite) GPU
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