Popular GitHub Repositories for Class

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

Machine Learning University: Accelerated Natural Language Processing Class

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

NestJS Helper + AI Chatbot Development

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

Machine Learning University: Accelerated Computer Vision Class

The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.

Introducing DeToxify – the ultimate toxicity buster. Armed with NLP superpowers, it sniffs out harmful comments, neutralizes negativity, and rewrites toxicity into pure class. Think of it as Jarvis for your online spaces—making the internet a kinder, smarter place, one comment at a time.

Prompty makes it easy to create, manage, debug, and evaluate LLM prompts for your AI applications. Prompty is an asset class and format for LLM prompts designed to enhance observability, understandability, and portability for developers.

PyCIL: A Python Toolbox for Class-Incremental Learning

[CVPR 2020] CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement

Neum AI is a best-in-class framework to manage the creation and synchronization of vector embeddings at large scale.

Eagle Family: Exploring Model Designs, Data Recipes and Training Strategies for Frontier-Class Multimodal LLMs

A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation

A PyTorch implementation of the Deep SVDD anomaly detection method

pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;同时也实现了目标检测faster r-cnn和retinanet两个网络的CAM图;欢迎试用、关注并反馈问题...

Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019

Slides, paper notes, class notes, blog posts, and research on ML ?, statistics ?, and AI ?.

EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.

Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.