Note

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Model Zoo Summary

• Number of papers: 34

  • ALGORITHM: 34

• Number of checkpoints: 224

  • [ALGORITHM] Conformer: Local Features Coupling Global Representations for Visual Recognition (4 ckpts)

  • [ALGORITHM] Patches Are All You Need? (3 ckpts)

  • [ALGORITHM] A ConvNet for the 2020s (13 ckpts)

  • [ALGORITHM] CSPNet: A New Backbone that can Enhance Learning Capability of CNN (3 ckpts)

  • [ALGORITHM] Residual Attention: A Simple but Effective Method for Multi-Label Recognition (1 ckpts)

  • [ALGORITHM] Training data-efficient image transformers & distillation through attention (9 ckpts)

  • [ALGORITHM] Densely Connected Convolutional Networks (4 ckpts)

  • [ALGORITHM] EfficientFormer: Vision Transformers at MobileNet Speed (3 ckpts)

  • [ALGORITHM] Rethinking Model Scaling for Convolutional Neural Networks (23 ckpts)

  • [ALGORITHM] HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions (9 ckpts)

  • [ALGORITHM] Deep High-Resolution Representation Learning for Visual Recognition (9 ckpts)

  • [ALGORITHM] MLP-Mixer: An all-MLP Architecture for Vision (2 ckpts)

  • [ALGORITHM] MobileNetV2: Inverted Residuals and Linear Bottlenecks (1 ckpts)

  • [ALGORITHM] Searching for MobileNetV3 (2 ckpts)

  • [ALGORITHM] MViTv2: Improved Multiscale Vision Transformers for Classification and Detection (4 ckpts)

  • [ALGORITHM] MetaFormer is Actually What You Need for Vision (5 ckpts)

  • [ALGORITHM] Designing Network Design Spaces (16 ckpts)

  • [ALGORITHM] RepMLP: Re-parameterizing Convolutions into Fully-connected Layers forImage Recognition (2 ckpts)

  • [ALGORITHM] Repvgg: Making vgg-style convnets great again (12 ckpts)

  • [ALGORITHM] Res2Net: A New Multi-scale Backbone Architecture (3 ckpts)

  • [ALGORITHM] Deep Residual Learning for Image Recognition (26 ckpts)

  • [ALGORITHM] Aggregated Residual Transformations for Deep Neural Networks (4 ckpts)

  • [ALGORITHM] Squeeze-and-Excitation Networks (2 ckpts)

  • [ALGORITHM] ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices (1 ckpts)

  • [ALGORITHM] Shufflenet v2: Practical guidelines for efficient cnn architecture design (1 ckpts)

  • [ALGORITHM] Swin Transformer: Hierarchical Vision Transformer using Shifted Windows (14 ckpts)

  • [ALGORITHM] Swin Transformer V2: Scaling Up Capacity and Resolution (12 ckpts)

  • [ALGORITHM] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet (3 ckpts)

  • [ALGORITHM] Transformer in Transformer (1 ckpts)

  • [ALGORITHM] Twins: Revisiting the Design of Spatial Attention in Vision Transformers (6 ckpts)

  • [ALGORITHM] Visual Attention Network (8 ckpts)

  • [ALGORITHM] Very Deep Convolutional Networks for Large-Scale Image Recognition (8 ckpts)

  • [ALGORITHM] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (7 ckpts)

  • [ALGORITHM] Wide Residual Networks (3 ckpts)