Popular GitHub Repositories for Classifiers Comparison

A reference containing Styles and Keywords that you can use with MidJourney AI. There are also pages showing resolution comparison, image weights, and much more!

Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go.

Facilitating the design, comparison and sharing of deep text matching models.

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Analysis, Comparison, Trends, Rankings of Open Source Software, you can also get insight from more than 7 billion with natural language (powered by OpenAI). Follow us on Twitter: https://twitter.com/ossinsight

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Integrate deep learning models for image classification | Backbone learning/comparison/magic modification project

Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.

ARCH models in Python

Human ChatGPT Comparison Corpus (HC3), Detectors, and more! ?

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

LeGO-LOAM, LIO-SAM, LVI-SAM, FAST-LIO2, Faster-LIO, VoxelMap, R3LIVE, Point-LIO, KISS-ICP, DLO, DLIO, Ada-LIO, PV-LIO, SLAMesh, ImMesh, FAST-LIO-MULTI, M-LOAM, LOCUS, SLICT, MA-LIO, CT-ICP, GenZ-ICP, iG-LIO, SR-LIO application and comparison on Gazebo and real-world datasets. Installation and config files are provided.

? The experiment tracker for foundation model training

Code for training and test machine learning classifiers on MIT-BIH Arrhyhtmia database

Python package for conformal prediction

Java免费离线AI算法工具箱，支持人脸识别(人脸比对，人脸库查询(Milvus/SQLite)，人脸属性检测，活体检测)、目标检测(支持 YOLO，SSD、自训练模型)、OCR文字识别、机器翻译（200+语言翻译）等功能，致力于为开发者提供开箱即用的 AI 能力，无需 Python 环境，Maven 引用即可使用。已集成 SeetaFace6、YOLOv8、PaddleOCR 等主流模型

Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa

Facilitating the design, comparison and sharing of deep text matching models.

Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking

Face recognition SDK Android with 3D passive face liveness detection: face recognition by face matching, face compare, face comparison, face identification, face anti-spoofing, face identity, facial recognition, face representation, face reconstruction, and face liveness detection for IDV