| --- |
| license: apache-2.0 |
| library_name: libreyolo |
| pipeline_tag: image-classification |
| datasets: |
| - imagenet-1k |
| tags: |
| - image-classification |
| - efficientnetv2 |
| - libreyolo |
| --- |
| |
| # LibreEfficientNetV2b2-cls |
|
|
| EfficientNetV2-base-b2 image classifier (1000-class ImageNet-1k), repackaged |
| for [LibreYOLO](https://github.com/LibreYOLO/libreyolo). Eval resolution |
| 260px; timm-reported top-1 accuracy ~80.5%. |
|
|
| ## Usage |
|
|
| ```python |
| from libreyolo import LibreYOLO |
| |
| model = LibreYOLO("LibreEfficientNetV2b2-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_b2.in1k`](https://huggingface.co/timm/tf_efficientnetv2_b2.in1k) in |
| [huggingface/pytorch-image-models](https://github.com/huggingface/pytorch-image-models). |
| Copyright (c) 2019 Ross Wightman. Licensed under the Apache License 2.0. |
|
|
| Original architecture: EfficientNetV2 by Google |
| ([google/automl](https://github.com/google/automl/tree/master/efficientnetv2)), |
| "EfficientNetV2: Smaller Models and Faster Training" |
| ([arXiv:2104.00298](https://arxiv.org/abs/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](https://github.com/LibreYOLO/libreyolo). |
|
|
| ## License |
|
|
| Apache License 2.0. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) |
| files in this repository. |
|
|
