Model reference · open weights
LVFace is an open-weight embedding model from bytedance-research, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface&logoColor=yellow" alt="Model" / src="https://img.shields.io/badge/Code-GitHub-orange?logo=github&logoColor=white" alt="Code" / src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" / This is the official PyTorch implementation for the inference of LVFace. Drawing inspiration from the massive data support, multi-stage training paradigm, and Transformer architecture of large-model technology, this method, based on Large Vision Transformer, has carried out progressive optimization of the face clustering space through multiple stages on massive datasets. 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: 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: Step-by-Step Usage Guide 1. Installation & Environment Setup First, clone the repository and navigate to the project directory: Then install all required dependencies using the provided 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., ./LVFacemodel/). 3. Run Inference Execute the inferenceonnx.py script to perform feature extraction and similarity calculation. A complete workflow example: Note: The LVFaceONNXInferencer class is defined in inferenceonnx.py, which handles ONNX model loading, image preprocessing, feature
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | bytedance-research |
|---|---|
| Type | Embedding models |
| Variants | 1 |
| Runs with | lvface |
| Released | 2025-08-10 |
| Popularity | 33k downloads / month |
| Likes | 31 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| LVFace | — | BF16 | — | — | Weights ↗ |
Using it via the API
Once AxForge deploys lvface for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (lvface below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"lvface","input":"text to embed"}'
Licence
Open weights under mit — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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