| ---
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| license: apache-2.0
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| library_name: libreyolo
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| pipeline_tag: image-classification
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| tags:
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| - image-classification
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| - resnet
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| - imagenet
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| - libreyolo
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| datasets:
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| - imagenet-1k
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| ---
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|
|
| # LibreResNet50-cls
|
|
|
| ResNet-50 image classifier (224px, ImageNet-1k, 1000 classes), repackaged for
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| [LibreYOLO](https://github.com/LibreYOLO/libreyolo). ~25.6M parameters.
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|
|
| ## Source
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|
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| Derived from [huggingface/pytorch-image-models (timm)](https://github.com/huggingface/pytorch-image-models),
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| model `resnet50.a1_in1k` (the "ResNet Strikes Back" A1 recipe, ImageNet-1k).
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| Architecture: He et al. 2015 (https://arxiv.org/abs/1512.03385). Weights by Ross
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| Wightman and the timm contributors. Copyright (c) Ross Wightman. Licensed under the
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| Apache License 2.0.
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|
|
| ## Modifications
|
|
|
| Learned parameters are unchanged from timm. The checkpoint is metadata-wrapped into
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| the LibreYOLO format (model_family / task / nc / names). LibreYOLO's native ResNet
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| (v1.5) mirrors timm/torchvision module names, so inference is bit-identical to timm
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| (max_abs_diff == 0, verified by the parity test). See
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| `weights/convert_resnet_weights.py` in the
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| [LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo).
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|
|
| ## Usage
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|
|
| ```python
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| from libreyolo import LibreYOLO
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|
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| model = LibreYOLO("LibreResNet50-cls.pt")
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| result = model.predict("image.jpg")[0]
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| print(result.probs.top1, result.probs.top5)
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| ```
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|
|
| ## License
|
|
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| Apache License 2.0. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files in this repository.
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| |
