ConvNeXt V2

ConvNeXt V2 is an image classifier with global response normalization.

任务: classification
安装: pip install libreyolo
支持层级: 已支持，自 v1.6.0 起。起支撑作用的可训练家族：在 CI 里保持绿色，功能视情况落地。
上游: ConvNeXt V2，由 Meta AI 发布，采用 MIT code; CC-BY-NC-4.0 weights 许可。论文、源码
许可: 代码采用 MIT，权重采用 MIT code; CC-BY-NC-4.0 weights。商用

Install

bash

pip install "libreyolo"

Predict

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreConvNeXtV2atto-cls.pt", device="cpu")result = model(SAMPLE_IMAGE)print(result.probs)

The eight sizes run classification at 224 pixels. Official pretrained weights are CC-BY-NC-4.0.

Train

Training rebuilds the classification head for an ImageFolder dataset. cls_pw accepts values from 0 to 1 and defaults to 0. class_weights=True selects the alternative sample-normalized weighting; it cannot combine with cls_pw>0. Weighting settings must match when resuming.

Dataset setup describes the required training data.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreConvNeXtV2atto-cls.pt", device="cpu")# "smoke10" auto-downloads; for your own data, pass a folder with train/ and val/ class subfolders.model.train(data="smoke10", epochs=1, device="cpu", workers=0)

Validate

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreConvNeXtV2atto-cls.pt", device="cpu")metrics = model.val(data="smoke10", workers=0)print(metrics)

Use the dataset format for this task. Validation explains the dataset requirements and returned metrics.

Export

任务	ONNX	TorchScript	ExecuTorch	TensorRT	OpenVINO	Paddle	MNN	RKNN	ncnn	TFLite	CoreML	Core AI
Classification	Classification to ONNX：支持	Classification to TorchScript：支持	Classification to ExecuTorch：支持	Classification to TensorRT：支持	Classification to OpenVINO：支持	Classification to Paddle：不支持	Classification to MNN：不支持	Classification to RKNN：不支持	Classification to ncnn：支持	Classification to TFLite：不支持	Classification to CoreML：不支持	Classification to Core AI：不支持

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreConvNeXtV2atto-cls.pt", device="cpu")model.export(format="onnx")

Export setup lists format dependencies and loading exported artifacts.

Checkpoints

文件	权重许可
Classification
LibreConvNeXtV2atto-cls.pt	cc-by-nc-4.0
LibreConvNeXtV2femto-cls.pt	cc-by-nc-4.0
LibreConvNeXtV2pico-cls.pt	cc-by-nc-4.0
LibreConvNeXtV2n-cls.pt	cc-by-nc-4.0
LibreConvNeXtV2t-cls.pt	cc-by-nc-4.0
LibreConvNeXtV2b-cls.pt	cc-by-nc-4.0
LibreConvNeXtV2l-cls.pt	cc-by-nc-4.0
LibreConvNeXtV2h-cls.pt	cc-by-nc-4.0

上面的每个文件目前都在 LibreYOLO 组织中，并会在首次使用时下载。

Licensing

请检查你所下载的具体权重在 Hugging Face 仓库中的许可。LibreYOLO 组织里的每个检查点都附有许可，同一家族内也不一定相同。该仓库是权威来源；以下摘要说明本页上次验证时适用的情况。

这里只说明涉及的许可证，不构成法律意见。如果答案对商用很重要，请自行阅读许可证并咨询法律顾问。

原始工作: ConvNeXt V2, Meta AI
上游许可: MIT code; CC-BY-NC-4.0 weights
上游来源: github.com/facebookresearch/ConvNeXt-V2
LibreYOLO 代码: MIT
权重: 采用 MIT code; CC-BY-NC-4.0 weights 许可，重新发布在 huggingface.co/LibreYOLO
解读: The source code is MIT. Official pretrained weights are CC-BY-NC-4.0 and exclude commercial use.

Citation

@article{Woo2023ConvNeXtV2,
  title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
  author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie},
  year={2023},
  journal={arXiv preprint arXiv:2301.00808},
}

复制自作者在 raw.githubusercontent.com/facebookresearch/ConvNeXt-V2/main/README.md 上提供的引用块。