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[ECCV 2024] Official Implementation and Dataset Release for <A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization>
Training library for local feature detection and matching
A toolbox for mapping and localization with line features.
Implementation of the paper "DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients"
The official PyTorch implementation of the paper: *Xili Dai, Xiaojun Yuan, Haigang Gong, Yi Ma. "Fully Convolutional Line Parsing." *.
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。
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.
torch-optimizer -- collection of optimizers for Pytorch
PC-DARTS:Partial Channel Connections for Memory-Efficient Differentiable Architecture Search
[ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang
A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.
👶🏻 신입 개발자 전공 지식 & 기술 면접 백과사전 📖
Unofficial Pytorch code for "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence" in NeurIPS'20. This repo contains reproduced checkpoints.
[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)
Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
Image-to-Image Translation in PyTorch
A collection of awesome resources image-to-image translation.
This is the implementation of our paper in ECCV 2020.
Model interpretability and understanding for PyTorch
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.