LVFace Model

README.md

@@ -39,8 +39,6 @@ This is the official PyTorch implementation for the inference of LVFace. Drawing
 <!-- </h5> -->
 </div>
 
-<br>
-
 <div align="center">
 <img src="docs/LVFace.png" alt="LVFace 示意图" width="80%">
 </div>
@@ -53,7 +51,6 @@ This is the official PyTorch implementation for the inference of LVFace. Drawing
 - 🎉🎉🎉 LVFace is accepted by **ICCV 2025**. (July, 2025 UTC)
 - 🔥🔥🔥 We have updated the arXiv report of LVFace. Please click [here](https://arxiv.org/abs/2501.13420v2) 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)
-
 <div align="center">
 <img src="docs/MFR.png" alt="LVFace 示意图" width="30%">
 </div>
@@ -73,13 +70,11 @@ 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](https://drive.google.com/file/d/1aC4zf2Bn0xCVH_ZtEuQipR2JvRb1bf8o/view)  
 - **MFR-Ongoing**: [Challenge Page](https://insightface.ai/mfr_ongoing)  
 
 ## 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](https://huggingface.co/bytedance-research/LVFace/tree/main/LVFace-T_Glint360K) |
@@ -92,7 +87,7 @@ Pretrained model weights for inference are available below in both ONNX and PyTo
 ## Step-by-Step Usage Guide
 
 **1. Installation & Environment Setup**
-  
+
    First, clone the repository and navigate to the project directory:
    ```bash
    # Clone the LVFace repository
@@ -135,17 +130,13 @@ Pretrained model weights for inference are available below in both ONNX and PyTo
    **Note**: The `LVFaceONNXInferencer` class is defined in `inference_onnx.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`).  
-
 2. 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 \
@@ -156,7 +147,6 @@ python eval_ijbc.py \
 
 ## 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).
 
 
@@ -173,5 +163,4 @@ If you find this work useful, please cite our paper and give us a star ⭐:
 
 ## Acknowledgments  
 We sincerely thank **Professor Deng Jiankang** for his valuable guidance and insights throughout the research. 
-
 We also appreciate the [InsightFace](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch) for their excellent and research support.