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LibreResNet50 Cls

ResNet-50 image classifier (224px, ImageNet-1k, 1000 classes), repackaged for LibreYOLO. 25.6M parameters.

KindOtherVersionRevision 1961a87Cgrade

Model source

Kind
Other
File size
97.8 MB
Version
vRevision 1961a87
Updated
Sep 17, 2026
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0
Source
Hugging Face

Source description

LibreResNet50-cls

ResNet-50 image classifier (224px, ImageNet-1k, 1000 classes), repackaged for LibreYOLO. ~25.6M parameters.

Source

Derived from huggingface/pytorch-image-models (timm), model resnet50.a1_in1k (the "ResNet Strikes Back" A1 recipe, ImageNet-1k). Architecture: He et al. 2015 (https://arxiv.org/abs/1512.03385). Weights by Ross Wightman and the timm contributors. Copyright (c) Ross Wightman. Licensed under the Apache License 2.0.

Modifications

Learned parameters are unchanged from timm. The checkpoint is metadata-wrapped into the LibreYOLO format (model_family / task / nc / names). LibreYOLO's native ResNet (v1.5) mirrors timm/torchvision module names, so inference is bit-identical to timm (max_abs_diff == 0, verified by the parity test). See weights/convert_resnet_weights.py in the LibreYOLO source repository.

Usage

from libreyolo import LibreYOLO

model = LibreYOLO("LibreResNet50-cls.pt")
result = model.predict("image.jpg")[0]
print(result.probs.top1, result.probs.top5)

License

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

Sources

1 source
Hugging FacePrimaryVersion 1961a87c89bd6722fbad0bf895fc435a9fc0b1a6
huggingface.co/LibreYOLO/LibreResNet50-cls/tree/1961a87c89bd6722fbad0bf895fc435a9fc0b1a6

Verified Sep 2

Model artifacts

1 artifact

LibreResNet50-cls.pt

pt · 97.8 MB · SHA-256 a2a8ce977de6…34d6 · Hugging Face

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Source excerpts

2 excerpts

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

Source context: 0 downloads · 0 likes · Pipeline image-classification · Library libreyolo · Repo LibreYOLO/LibreResNet50-cls