[ICCV 2025 Highlight] LVFace: Progressive Cluster Optimization for Large Vision Models in Face Recognition

If you like LVFace, please give us a star ⭐ on GitHub for the latest update.

News

• 🔥🔥🔥 We have released the training weights of LVFace. Please click here to download it. (August, 2025 UTC)
• 🎉🎉🎉 LVFace has been recommended as ICCV Highlight. (July, 2025 UTC)
• 🎉🎉🎉 LVFace is accepted by ICCV 2025. (July, 2025 UTC)
• 🔥🔥🔥 We have updated the arXiv report of LVFace. Please click here to view it. (March, 2025 UTC)
• 🎉🎉🎉 LVFace secured 1st place in the ICCV 2021 Masked Face Recognition (MFR)-Ongoing Challenge (academic track). (December, 2024 UTC)
  

Requirements

All required dependencies are listed in requirements.txt:

txt onnxruntime-gpu==1.19.2 torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 scikit-image insightface numpy==1.23

Install all dependencies with a single command: pip install -r requirements.txt

Datasets

Test datasets for inference validation can be downloaded from the following sources:

• IJB-C & IJB-B: Google Drive
  
• MFR-Ongoing: Challenge Page
  

LVFace Pretrained Models

Pretrained model weights for inference are available below in both ONNX and PyTorch (.pt) formats:

| Training Data | Model | IJB-C(1e-6) | IJB-C(1e-5) | IJB-C(1e-4) | IJB-B(1e-4) | Download | |---------------|---------------|-------------|-------------|-------------|-------------|---------------| | Glint360K | LVFace-T | 88.53 | 95.63 | 96.67 | 95.41 | HuggingFace | | Glint360K | LVFace-S | 90.06 | 96.52 | 97.31 | 96.14 | HuggingFace | | Glint360K | LVFace-B | 90.06 | 97.00 | 97.70 | 96.51 | HuggingFace | | Glint360K | LVFace-L | 89.51 | 97.02 | 97.66 | 96.51 | HuggingFace | | WebFace42M | LVFace-B | - | - | - | - | come soon |

Step-by-Step Usage Guide

1. Installation & Environment Setup

First, clone the repository and navigate to the project directory: ```bash # Clone the LVFace repository git clone https://github.com/bytedance/LVFace.git

# Enter the project directory cd LVFace ```

Then install all required dependencies using the provided requirements.txt: bash # Install dependencies pip install -r requirements.txt

2. Download Pretrained Models
Download the ONNX-format pretrained weights from the LVFace Pretrained Models section, then place them in a directory (e.g., ./LVFace_model/).

3. Run Inference
Execute the inference_onnx.py script to perform feature extraction and similarity calculation. A complete workflow example:
```python ## Initialize the inferencer from inferenceonnx.py inferencer = LVFaceONNXInferencer( modelpath="./models/vitbdp005mask005.onnx", # Path to your ONNX model use_gpu=True # Set to False for CPU-only inference )

## Extract feature from local image feat1 = inferencer.inferfromimage("path/to/image1.jpg")

## Extract feature from URL feat2 = inferencer.inferfromurl("https://example.com/image1.jpg")

## Calculate cosine similarity similarity = inferencer.calculatesimilarity(feat1, feat2) print(f"Similarity score: {similarity:.6f}") # Output example: 0.892345 `` **Note**: TheLVFaceONNXInferencerclass is defined ininferenceonnx.py`, which handles ONNX model loading, image preprocessing, feature extraction, and similarity calculation in a unified interface. Ensure the model path and image paths are correctly specified before running.

Model Evaluation

Evaluation Steps 1. Modify the test dataset path (e.g., IJB-C, IJB-B) in the corresponding evaluation script (eval_ijbc.py).

1. Run the evaluation with pretrained model weights using the commands below:
   

Evaluation Commands

bash python eval_ijbc.py \ --model-prefix path/to/LVFace-B_Glint360K.pt \ --result-dir results \ --network vit_b_dp005_mask_005 > LVFace-B_Glint360K.log 2>&1 &

License

The code of LVFace is released under the MIT License. There is no limitation for both academic and commercial usage.

The models downloaded from our repo follow the above license policy (which is for non-commercial research purposes only).

Citation

If you find this work useful, please cite our paper and give us a star ⭐:
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} }

Acknowledgments

We sincerely thank Professor Jiankang Deng for his valuable guidance and insights throughout the research.

We also appreciate the InsightFace for their excellent and research support.