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Face.evolve

Face.evoLVe is an MIT-licensed Python library for face recognition and related computer-vision work, described as based on PaddlePaddle and PyTorch. Its repository documents face detection, landmark localization, affine alignment, image-data processing and model-training components.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Face.evolve — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a codebase for face-recognition research or a documented way to detect, landmark and align face images before further model work.

Who it is for

  • Researchers preparing face-image datasets for recognition experiments.
  • Developers exploring face detection, landmark localization and alignment workflows.
  • Practitioners developing or adapting face-recognition models with the documented framework ecosystem.

Documented uses

  • Organize face images by identity for training, validation or testing.
  • Detect faces and locate facial landmarks in an image.
  • Align images across a database folder and save the results in a parallel directory structure.
  • Explore model backbones, loss functions and data-processing methods for face-recognition research.
Before you choose

Requirements & limits

The repository is public and its code is released under the MIT License.

  • The README describes both PaddlePaddle and PyTorch, but the supplied usage excerpt does not provide dependency installation steps or versions.
  • The documented alignment interface processes a database folder and preserves its directory structure in the destination.
  • The README describes a multi-GPU PyTorch training scheme with the final fully connected/softmax layer computed on one master, noting this can struggle with very large identity sets.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare and align an identity-folder image collection

Set up a face-image collection and run the documented alignment script to save aligned images in a matching folder structure.

Before you start

  • A cloned copy of the repository.
  • Training, validation or test images arranged in folders by identity, with image files inside each identity folder.
  • Directories for data, checkpoints and logs, as described in the README.

Steps

  1. Clone the repository using the README’s documented repository URL.

  2. Create the data, checkpoint and log directories in an appropriate location.

  3. Arrange each database as a root folder containing one subfolder per identity, with that identity’s images inside.

  4. Use the documented face_align.py interface with source_root, dest_root and crop_size to process the folder and save aligned results.

Expected outcome

An aligned image collection saved under a destination folder that follows the source collection’s directory structure.

Guide limitations

  • The documented guide covers alignment and data organization, not training or deploying a recognition application.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
ZhaoJ9014/face.evoLVe · Official homepage title · retained in its source language
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