# ZhaoJ9014/face.evoLVe

🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

Repository: https://github.com/ZhaoJ9014/face.evoLVe
Canonical: https://ross.abutalabs.com/products/faceevolve
Language: Python
License: MIT
License Family: permissive
Topics: pytorch, face-recognition, face-detection, face-alignment, face-landmark-detection, model-training, feature-extraction, fine-tuning, data-augmentation, deep-learning, computer-vision, imbalanced-learning, transfer-learning, hard-negative-mining, supervised-learning, nus, tencent, convolutional-neural-network, machine-learning, artificial-intelligence
Last push: 2025-03-20T02:45:06+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 12, release rhythm 35, longevity 100
- inputs: {"age_days": 2799, "days_push": 531, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3589, forks 760 (observed 2026-08-28T04:08:11.173292+00:00)

## What it is
A high-performance face recognition library built on PaddlePaddle and PyTorch, providing comprehensive tools for face-related analytics and applications. It offers training code, model architectures, and datasets for tasks like face detection, alignment, landmark detection, and verification.

## Use cases
- recognize faces in images
- detect and align faces
- extract facial landmarks
- train a face recognition model
- fine-tune a pretrained face model
- perform face anti-spoofing / liveness detection
- build a face verification system
- run large-scale face recognition with millions of identities

## When to choose
- You need a comprehensive, high-performance face recognition library with training and inference capabilities
- You want to leverage PyTorch or PaddlePaddle for face-related analytics
- You require distributed multi-GPU training for large-scale face recognition
- You need access to curated face datasets and model architectures like IR-152

## When to avoid
- You need general-purpose object detection or image classification beyond faces
- You require a lightweight, production-ready face recognition API without training needs
- You are working with non-face biometric recognition (e.g., fingerprints, irises)
- You need real-time face recognition on edge devices with limited compute

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing, llm-training
- domain: computer-vision, deep-learning, artificial-intelligence, machine-learning
- platform: python, cross-platform
- tags: face-recognition, face-detection, face-alignment, face-landmark-detection, feature-extraction, fine-tuning, transfer-learning, data-augmentation, imbalanced-learning, hard-negative-mining, model-training, pytorch, paddlepaddle, computer-vision, deep-learning, face-analytics, face-verification, face-identification, face-anti-spoofing, liveness-detection, distributed-training, multi-gpu, ms-celeb-1m, ir-152, nus, tencent, mit-license, gpu

## Member repositories
- ZhaoJ9014/face.evoLVe (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:11.173292+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:33:52.343347+00:00, confidence not recorded.
  - readme: https://github.com/ZhaoJ9014/face.evoLVe (fetched 2026-08-28T04:08:11.173292+00:00, sha ad396357c47b)
- Data as of 2026-08-30T08:39:29.467469+00:00.