ResNet

Install

ResNet needs no optional extra. Everything it imports is in the base install.

pip install libreyolo

Predict

Weights download from Hugging Face on first use and are cached locally.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreResNet50-cls.pt")result = model(SAMPLE_IMAGE, save=True) print(result.probs.top1, result.probs.top1conf)print(result.probs.top5)

libreyolo predict model=LibreResNet50-cls.pt source=cat.jpg save=True

The returned Results object is the one every family returns, so swapping in a different model is a one line change. A classifier carries no boxes or masks: result.probs holds the whole-image prediction, with top1, top5, top1conf and top5conf. conf, iou and max_det are accepted for API parity but have no effect, since there is nothing to threshold or suppress on a single probability vector. See prediction for sources, streaming and result handling.

Variants

Four depths, all trained and evaluated the same way, so picking one is a straight parameter-count-for-accuracy trade. The task is fixed: every size covers classification only. The weights filename ends -cls.pt on every size, and that suffix is what the factory reads to route to this family; no task= argument is needed.

Train

Fine-tuning starts from the published ImageNet backbone and rebuilds the final classifier layer to the target dataset's class count automatically.

from libreyolo import LibreYOLO model = LibreYOLO("LibreResNet50-cls.pt")model.train(data="imagenette160", epochs=5)

libreyolo train model=LibreResNet50-cls.pt data=imagenette160 epochs=5

libreyolo train model=LibreResNet50-cls.pt data=imagenette160 \  epochs=50 device=0,1 batch=-1

Left alone, the trainer runs 100 epochs at lr0=1e-3 with AdamW, a batch of 64 and early stopping after 50 epochs without improvement. data accepts a dataset root (train/ and val/, one folder per class), a known short name such as imagenette160, or a .zip URL. lora=True is not supported here; passing it raises, since LoRA in LibreYOLO targets transformer components with nn.Linear layers and ResNet has none.

See training for datasets, augmentation, multi-GPU and loggers.

Validate

val() returns a dictionary of metrics/ keys. For classification that is top-1 and top-5 accuracy over the validation split.

from libreyolo import LibreYOLO model = LibreYOLO("LibreResNet50-cls.pt")metrics = model.val(data="imagenette160") print(metrics["metrics/accuracy_top1"])print(metrics["metrics/accuracy_top5"])

libreyolo val model=LibreResNet50-cls.pt data=imagenette160

Export

An exported artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .engine file behaves like a checkpoint and returns the same Results. Export lists the arguments every format accepts and the extras a few of them add.

from libreyolo import LibreYOLO model = LibreYOLO("LibreResNet50-cls.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

libreyolo export model=LibreResNet50-cls.pt format=onnxlibreyolo export model=LibreResNet50-cls.pt format=tensorrt half=True

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The factory routes on the file suffix, so an exported artifact loads# like any checkpoint and returns the same Results object.model = LibreYOLO("LibreResNet50-cls.onnx")result = model(SAMPLE_IMAGE) print(result.probs.top1)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@article{He2015,
	author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
	title = {Deep Residual Learning for Image Recognition},
	journal = {arXiv preprint arXiv:1512.03385},
	year = {2015}
}