[NeurIPS '25 Spotlight] Official Pytorch implementation of "Vision Transformers Don't Need Trained Registers"

[NeurIPS 2025] Pytorch Implementation for NeurIPS 2025 paper: ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining

An open source implementation of CLIP.

Accurate reimplementation of WinCLIP (pytorch version)

Official implementation for AnomalyCLIP (ICLR 2024)

[ICCV ADFM'25] ADer is an open source visual anomaly detection toolbox based on PyTorch, which supports multiple popular AD datasets and approaches.

[CVPR 2024] Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation

PyTorch implementation of JiT https://arxiv.org/abs/2511.13720

This is a resouce list for low light image enhancement

collection of diffusion model papers categorized by their subareas

[NeurlPS 2023] A Dataset and Benchmark for Pose-agnostic Anomaly Detection.

A curated list of papers & resources on anomaly detection foundation models using large language model, vision-language model, graph foundation model, time series foundation model, etc

[NeurIPS 2024 Spotlight] Pytorch Implementation for NeurIPS 2024 paper: ResAD: A Simple Framework for Class Generalizable Anomaly Detection

BertViz: Visualize Attention in Transformer Models

LangChain, LangGraph Open Tutorial for everyone!

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM …

Machine Learning Engineering Open Book

Collection of awesome test-time (domain/batch/instance) adaptation methods

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

[NeurIPS 2022 Spotlight] A Unified Model for Multi-class Anomaly Detection

The official repo for ”[WACV2025] Towards Accurate Unified Anomaly Segmentation“

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

A python library for user-friendly forecasting and anomaly detection on time series.

A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.

Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!