LibreConvNeXtb Cls

--- license: apache-2.0 library_name: libreyolo pipeline_tag: image-classification tags: - image-classification - convnext - imagenet - libreyolo datasets: - imagenet-1k --- # LibreConvNeXtb-cls ConvNeXt-Base image classifier (224px input, ImageNet-1k, 1000 classes), repackaged for [LibreYOLO](https://github.com/LibreYOLO/libreyolo). ~88.6M parameters. ## Source Derived from [huggingface/pytorch-image-models (timm)](https://github.com/huggingface/pytorch-image-models), model `convnext_base.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("LibreConvNeXtb-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.

Source context: 0 downloads · 0 likes · Pipeline image-classification · Library libreyolo · Repo LibreYOLO/LibreConvNeXtb-cls