mixup: Beyond Empirical Risk Minimization
Implementation of the mixup training method
Code for reproducing Manifold Mixup results (ICML 2019)
An implementation of "mixup: Beyond Empirical Risk Minimization"
Implementation of Adversarial Domain Adaptation with Domain Mixup (AAAI 2020 Oral).
An Android app for creating photo collages. This app demonstrates NavigationUI, Espresso testing, Robolectric testing, custom views, low-level UI manipulation, and more.
A PyTorch implementation of the paper Mixup: Beyond Empirical Risk Minimization in PyTorch
[Survey] Awesome List of Mixup Augmentation and Beyond (https://arxiv.org/abs/2409.05202)
Code for Recall@k Surrogate Loss with Large Batches and Similarity Mixup, CVPR 2022.
这里面存放了一些目标检测算法的数据增强方法。如mosaic、mixup。
This YOLOv5🚀😊 GUI road sign system uses MySQL💽, PyQt5🎨, PyTorch, CSS🌈. It has modules for login🔑, YOLOv5 setup📋, sign recognition🔍, database💾, and image processing🖼️. It supports diverse i...
For the Kaggle Competition on object detection with same name. 1) models used are DETR, EfficientDet, YOLOv5, RetinaNet, FasterRCNN. 2) Ensemble inference using Weighted Box Fusion 3) Pseudo Learning ...