convnext - 技术专题

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

OpenMMLab Detection Toolbox and Benchmark

OpenMMLab Pre-training Toolbox and Benchmark

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

[MICCAI 2023] MedNeXt is a fully ConvNeXt architecture for 3D medical image segmentation.

PASSL包含 SimCLR，MoCo v1/v2，BYOL，CLIP，PixPro，simsiam, SwAV, BEiT，MAE 等图像自监督算法以及 Vision Transformer，DEiT，Swin Transformer，CvT，T2T-ViT，MLP-Mixer，XCiT，ConvNeXt，PVTv2 等基础视觉算法

A lightweight end-to-end text-to-speech model

1st place solution of RSNA Screening Mammography Breast Cancer Detection competition on Kaggle: https://www.kaggle.com/competitions/rsna-breast-cancer-detection

A PyTorch-based Python library with UNet architecture and multiple backbones for Image Semantic Segmentation.

👁 2022年数字中国创新大赛 (DCIC 2022) 卫星应用赛题——海上船舶智能检测第三名解决方案

YOLOv5-ODConvNeXt is an improved version of YOLOv5 for ship detection on drone-captured images.

[NeurIPS 2023] Code for the paper "Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models"

provide some new architecture, channel pruning and quantization methods for yolov5

超清遥感图像的语义分割，内附类别不平衡损失为何失效

forex trading with reinforcement learning

😉 Face Recognition using Convnext model + Flask

医学影像深度学习诊断框架，支持多尺度特征提取、注意力机制和多种现代架构（ResNet/ConvNeXt/Swin），在胸部X光数据集上达到91.67%准确率 / Production-ready medical image diagnosis framework with multi-scale features, attention mechanisms, and modern architectures (ResNet/ConvNeXt/Swin), achieving 91.67% accuracy

Image Classification Tutorial: ConvNext--> 98.8% on CIFAR10 + 92.4% on CIFAR100; ResNet18 -- 95.6% on CIFAR10 + 79.1% on CIFAR100

Train torchvision's MaskRCNN model using the ConvNeXt architecture as the backbone network.

🥈12th place solution on G2Net Detecting Continuous Gravitational Waves🥈