JavaScript Style Guide

Principles of Writing Consistent, Idiomatic JavaScript

Irrigation blog

30 Day Vanilla JS Challenge

Curated list of project-based tutorials

AREFR(Adversarial Robustness Evaluation for Face Recognition) 人脸识别对抗鲁棒性评估平台

Code for the CVPR 2023 paper "Proximal Splitting Adversarial Attacks for Semantic Segmentation"

Pytorch implementation of FCOS Object Detection Paper [Work in Progress]

Object detection, 3D detection, and pose estimation using center point detection:

The official PyTorch implementation of "Adversarially-Aware Robust Object Detector"

Code, data and benchmark from the paper "Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming" (NeurIPS 2019 ML4AD)

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

[ICML 2023] "NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations" by Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang, Yingyan …

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

COCO API - Dataset @ http://cocodataset.org/

OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.

OpenMMLab Detection Toolbox and Benchmark

Code for the ICCV 2021 paper "Augmented Lagrangian Adversarial Attacks"

RobustBench: a standardized adversarial robustness benchmark [NeurIPS 2021 Benchmarks and Datasets Track]

Python client for Baidu Yun (Personal Cloud Storage) 百度云/百度网盘Python客户端

My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and F…

Code implemented for the bachelor thesis "Testing the robustness of autonomous driving systems"

Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

This is the git repo for Adversarial Patch Attacks for the Camouflaging of Pedestrians and Cars From Computer Vision Models in Autonomous Driving.

Library containing PyTorch implementations of various adversarial attacks and resources

[CVPR 2023] T-SEA: Transfer-based Self-Ensemble Attack on Object Detection

Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving, CVPR 2023