---
license: apache-2.0
library_name: libreyolo
pipeline_tag: image-classification
tags:
- image-classification
- convnext
- imagenet
- libreyolo
datasets:
- imagenet-1k
---

# LibreConvNeXtt-cls

ConvNeXt-Tiny image classifier (224px input, ImageNet-1k, 1000 classes),
repackaged for [LibreYOLO](https://github.com/LibreYOLO/libreyolo). ~28.6M parameters.

## Source

Derived from [huggingface/pytorch-image-models (timm)](https://github.com/huggingface/pytorch-image-models),
model `convnext_tiny.fb_in1k`. The ConvNeXt architecture is by Zhuang Liu et al. (Meta Platforms),
originally released at [facebookresearch/ConvNeXt](https://github.com/facebookresearch/ConvNeXt)
under the MIT License. The ImageNet-1k pretrained weights are redistributed by timm under the
Apache License 2.0 (no distillation, no extra data).
Copyright (c) Meta Platforms, Inc. and the timm contributors.

ConvNeXt paper: *A ConvNet for the 2020s* (https://arxiv.org/abs/2201.03545).

## Modifications

Learned parameters are **unchanged** from timm. The checkpoint is metadata-wrapped into the
LibreYOLO format (`model_family` / `task` / `nc` / `names`). LibreYOLO's native ConvNeXt
implementation mirrors timm's module names, so inference is **bit-identical** to timm
(`max_abs_diff == 0`, verified by the parity test). See
`weights/convert_convnext_weights.py` in the
[LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo).

## Usage

```python
from libreyolo import LibreYOLO

# Auto-downloads from this repo on first use.
model = LibreYOLO("LibreConvNeXtt-cls.pt")
result = model.predict("image.jpg")[0]
print(result.probs.top1, result.probs.top5)   # ImageNet-1k class id + top-5 ids
```

## License

Apache License 2.0. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files in this repository.

Only ConvNeXt **V1** is published here. ConvNeXt-V2's small checkpoints are CC-BY-NC and are not redistributed.