Popular GitHub Repositories for Defect Detection

YOLOv5 ? in PyTorch > ONNX > CoreML > TFLite

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

OpenMMLab Detection Toolbox and Benchmark

State-of-the-art 2D and 3D Face Analysis Project

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

deep learning for image processing including classification and object-detection etc.

Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.

CVPR 2025 论文和开源项目合集

JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

Face recognition using Tensorflow

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

An open source library for face detection in images. The face detection speed can reach 1000FPS.

cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.

A paper list of object detection using deep learning.

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.