LibreEfficientNetV2b3-cls

LibreEfficientNetV2b3-cls

EfficientNetV2-base-b3 image classifier (1000-class ImageNet-1k), repackaged for LibreYOLO. Eval resolution 300px; timm-reported top-1 accuracy ~82.1%.

Usage

from libreyolo import LibreYOLO

model = LibreYOLO("LibreEfficientNetV2b3-cls.pt")
result = model.predict("image.jpg")[0]
print(result.probs.top1, result.probs.top1conf)
print(result.probs.top5)

Source

Derived from the timm checkpoint tf_efficientnetv2_b3.in1k in huggingface/pytorch-image-models. Copyright (c) 2019 Ross Wightman. Licensed under the Apache License 2.0.

Original architecture: EfficientNetV2 by Google (google/automl), "EfficientNetV2: Smaller Models and Faster Training" (arXiv:2104.00298), Apache License 2.0. Only the ImageNet-1k checkpoint is published here — the ImageNet-21k / JFT variants carry extra-data terms and are intentionally excluded.

Modifications

State-dict key remapping only. Learned parameters are unchanged; inference is bit-identical to timm (max_abs_diff == 0). See weights/convert_efficientnetv2_weights.py in the LibreYOLO source repository.

License

Apache License 2.0. See the LICENSE and NOTICE files in this repository.