ConvNeXt

Install

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

pip install libreyolo

Adapter fine-tuning with lora=True is the exception, and needs the lora extra.

pip install "libreyolo[lora]"

Predict

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

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

libreyolo predict model=LibreConvNeXtt-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

Three sizes, tiny/small/base, 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("LibreConvNeXtt-cls.pt")model.train(data="imagenette160", epochs=5)

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

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

libreyolo train model=LibreConvNeXtt-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. ConvNeXt's blocks carry the nn.Linear MLPs LoRA needs, so lora=True is supported here, and injects adapters into the block MLPs rather than fine-tuning the full backbone.

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("LibreConvNeXtt-cls.pt")metrics = model.val(data="imagenette160") print(metrics["metrics/accuracy_top1"])print(metrics["metrics/accuracy_top5"])

libreyolo val model=LibreConvNeXtt-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("LibreConvNeXtt-cls.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

libreyolo export model=LibreConvNeXtt-cls.pt format=onnxlibreyolo export model=LibreConvNeXtt-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("LibreConvNeXtt-cls.onnx")result = model(SAMPLE_IMAGE) print(result.probs.top1)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@Article{liu2022convnet,
  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
  title   = {A ConvNet for the 2020s},
  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year    = {2022},
}