LibreMobileNetV4l Cls

--- license: apache-2.0 library_name: libreyolo pipeline_tag: image-classification tags: - image-classification - mobilenetv4 - imagenet - libreyolo datasets: - imagenet-1k --- # LibreMobileNetV4l-cls MobileNetV4-conv-Large image classifier (256px input, ImageNet-1k, 1000 classes), repackaged for [LibreYOLO](https://github.com/LibreYOLO/libreyolo). ~32.6M parameters. ## Source Derived from [huggingface/pytorch-image-models (timm)](https://github.com/huggingface/pytorch-image-models), model `mobilenetv4_conv_large.e500_r256_in1k`. Architecture and pretrained weights by Ross Wightman and the timm contributors (ImageNet-1k, trained in timm; no distillation, no extra data). Copyright (c) Ross Wightman. Licensed under the Apache License 2.0. MobileNetV4 paper: "MobileNetV4 - Universal Models for the Mobile Ecosystem" (https://arxiv.org/abs/2404.10518). ## Modifications Learned parameters are unchanged from timm. The checkpoint is metadata-wrapped into the LibreYOLO format (model_family / task / nc / names). LibreYOLO's native MobileNetV4 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_mobilenetv4_weights.py` in the [LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo). ## Usage ```python from libreyolo import LibreYOLO model = LibreYOLO("LibreMobileNetV4l-cls.pt") result = model.predict("image.jpg")[0] print(result.probs.top1, result.probs.top5) ``` ## License Apache License 2.0. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files in this repository.

Source context: 0 downloads · 0 likes · Pipeline image-classification · Library libreyolo · Repo LibreYOLO/LibreMobileNetV4l-cls