Instructions to use litert-community/efficientnet_b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/efficientnet_b2 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
- Notebooks
- Google Colab
- Kaggle
Replace dynamic-range int8 with weight-only int8
Same change as efficientnet_b1 (merged earlier today): the shipped dynamic-range int8 degrades accuracy on this architecture, while weight-only int8 is near-lossless at the same size reduction.
Measured against the torch float reference on real photos plus fixed random tensors at native resolution: dynamic min logit correlation 0.867 (top-1 2/6) vs weight-only 0.999 (top-1 6/6). The replacement file is quantized directly from this repository's published float32 file (litert quantize, recipe weight_only_wi8_afp32), so the weights are identical.
The model card is updated in the same commit: the metric entries for the removed dynamic file are dropped and a short quantized-variant note with the measured numbers is added.