Instructions to use Mowshon/lvface-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LVFace
How to use Mowshon/lvface-weights with LVFace:
from huggingface_hub import hf_hub_download from inference_onnx import LVFaceONNXInferencer model_path = hf_hub_download("Mowshon/lvface-weights", "LVFace-L_Glint360K/LVFace-L_Glint360K.onnx") inferencer = LVFaceONNXInferencer(model_path, use_gpu=True, timeout=300) img_path = 'path/to/image1.jpg' embedding = inferencer.infer_from_image(img_path) - Notebooks
- Google Colab
- Kaggle
| language: | |
| - en | |
| library_name: lvface | |
| pipeline_tag: image-feature-extraction | |
| pretty_name: LVFace ONNX Weights for Python | |
| license: other | |
| tags: | |
| - face-recognition | |
| - face-embedding | |
| - onnx | |
| - lvface | |
| # LVFace ONNX Weights for Python | |
| This repository is a preservation mirror of the released LVFace ONNX embedding weights for use | |
| with the [`lvface`](https://github.com/mowshon/lvface) Python | |
| package. | |
| It is not the official LVFace model repository and is not affiliated with or endorsed by | |
| ByteDance. The models were created by the LVFace authors and were obtained from the official | |
| [`bytedance-research/LVFace`](https://huggingface.co/bytedance-research/LVFace) repository at | |
| revision [`b12702ab1f5c721748e054a66dc90e1edd1f0724`](https://huggingface.co/bytedance-research/LVFace/tree/b12702ab1f5c721748e054a66dc90e1edd1f0724). | |
| The files are mirrored without modification. | |
| ## Files | |
| | Model | Repository path | Size | SHA-256 | | |
| |---|---|---:|---| | |
| | LVFace-T Glint360K | `LVFace-T_Glint360K.onnx` | 76,653,813 bytes | `bf8da0e1e93c432d9a1d874a9ba0990f5859f970e8864b3990f2f33d11f9cdb3` | | |
| | LVFace-S Glint360K | `LVFace-S_Glint360K.onnx` | 304,196,926 bytes | `cd09f27c82ce0a3633fb8b1966d779a7171b23aa4f14ca0de6edf9677573d119` | | |
| | LVFace-B Glint360K | `LVFace-B_Glint360K.onnx` | 455,533,594 bytes | `9d834ed8e927fd35b9123b2bf97c40aad05785b1f9ecfb1c4c1f6242d38d1382` | | |
| | LVFace-L Glint360K | `LVFace-L_Glint360K.onnx` | 1,022,938,188 bytes | `49389036a4a5b69e0efcddfe34839ac72c7a71ce6b4dc1b6821e2ac368c87063` | | |
| Only the ONNX weights required by the Python package are mirrored here. PyTorch checkpoints, | |
| evaluation outputs, and training code are not part of this repository. | |
| ## Use with the Python package | |
| Install LVFace with Hugging Face download support: | |
| ```bash | |
| python -m pip install "lvface[hub]" | |
| ``` | |
| The package resolves a model name to a revision-pinned ONNX file during recognizer construction, | |
| verifies its size and SHA-256, and reuses the local Hugging Face cache on subsequent calls: | |
| ```python | |
| from lvface import FaceRecognizer | |
| recognizer = FaceRecognizer("LVFace-T_Glint360K") | |
| embedding = recognizer.embed("portrait.jpg") | |
| ``` | |
| Users who already have a compatible ONNX file can bypass Hugging Face entirely: | |
| ```python | |
| from lvface import FaceRecognizer | |
| recognizer = FaceRecognizer("/path/to/LVFace-T_Glint360K.onnx") | |
| ``` | |
| The package registry must reference this repository's Hugging Face repository ID and a pinned | |
| commit revision. Do not use `main` as the production revision. | |
| ## Direct download | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| path = hf_hub_download( | |
| repo_id="Mowshon/lvface-weights", | |
| filename="LVFace-T_Glint360K.onnx", | |
| revision="83b567cd6a3fc34434667e4415b6125feceb39ea", | |
| ) | |
| ``` | |
| ## Provenance and integrity | |
| The checksums in the file table match the Git LFS SHA-256 values published by the official | |
| repository at the source revision. Consumers should pin this mirror to an immutable commit and | |
| validate both file size and SHA-256 before loading a model. | |
| ## License | |
| The upstream repository contains conflicting license statements. Its model-card metadata labels | |
| the repository as MIT and it includes an MIT license for code, while the prose states that the | |
| downloaded models are for non-commercial research purposes only. | |
| This mirror does not resolve that conflict and does not grant any additional rights. Users and | |
| redistributors must review the | |
| [official model card](https://huggingface.co/bytedance-research/LVFace/blob/main/README.md#license) | |
| and obtain clarification or permission from the LVFace authors when needed. `LICENSE_CODE.txt` | |
| applies to upstream code and must not be interpreted as a separate license grant for the model | |
| weights. | |
| ## Required citation | |
| Use of these weights must include citation of the original LVFace work: | |
| ```bibtex | |
| @inproceedings{you2025lvface, | |
| title={{LVFace}: Progressive Cluster Optimization for Large Vision Models in Face Recognition}, | |
| author={You, Jinghan and Li, Shanglin and Sun, Yuanrui and Wei, Jiangchuan and Guo, Mingyu and Feng, Chao and Ran, Jiao}, | |
| booktitle={ICCV}, | |
| year={2025} | |
| } | |
| ``` | |
| Paper: [arXiv:2501.13420](https://arxiv.org/abs/2501.13420) | |
| ## Original resources | |
| - [Official LVFace model repository](https://huggingface.co/bytedance-research/LVFace) | |
| - [Official LVFace source repository](https://github.com/bytedance/LVFace) | |
| - [LVFace paper](https://arxiv.org/abs/2501.13420) | |