Deep Residual Learning for Image Recognition
Paper • 1512.03385 • Published • 17
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.