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
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. π26 knowledge distillation methods presented at TPAMI, CVPR, ICLR, ECCV, NeurIPS, ICCV, AAAI, etc are implemented so far. π Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
SSOG-Attention: Near-linear Visual-Attention and competitive results on ImageNet.