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)