Overview

  • Number of checkpoints: 223
  • Number of configs: 223
  • Number of papers: 26
    • ALGORITHM: 16
    • BACKBONE: 10

For supported datasets, see datasets overview.

Animal Models

  • Number of checkpoints: 34
  • Number of configs: 34
  • Number of papers: 2
    • [ALGORITHM] Deep High-Resolution Representation Learning for Human Pose Estimation (⇨)
    • [ALGORITHM] Simple Baselines for Human Pose Estimation and Tracking (⇨)

Bottom Up Models

  • Number of checkpoints: 18
  • Number of configs: 18
  • Number of papers: 6
    • [ALGORITHM] Associative Embedding: End-to-End Learning for Joint Detection and Grouping (⇨ ⇨ ⇨)
    • [ALGORITHM] Deep High-Resolution Representation Learning for Human Pose Estimation (⇨)
    • [ALGORITHM] Higherhrnet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation (⇨)
    • [ALGORITHM] The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation (⇨)
    • [BACKBONE] Deep Residual Learning for Image Recognition (⇨)
    • [BACKBONE] Mobilenetv2: Inverted Residuals and Linear Bottlenecks (⇨)

Face Models

  • Number of checkpoints: 6
  • Number of configs: 6
  • Number of papers: 5
    • [ALGORITHM] Deep High-Resolution Representation Learning for Visual Recognition (⇨)
    • [ALGORITHM] Deeppose: Human Pose Estimation via Deep Neural Networks (⇨)
    • [ALGORITHM] Distribution-Aware Coordinate Representation for Human Pose Estimation (⇨)
    • [ALGORITHM] Simple Baselines for Human Pose Estimation and Tracking (⇨)
    • [ALGORITHM] Wing Loss for Robust Facial Landmark Localisation With Convolutional Neural Networks (⇨)

Fashion Models

  • Number of checkpoints: 6
  • Number of configs: 6
  • Number of papers: 2
    • [ALGORITHM] Deeppose: Human Pose Estimation via Deep Neural Networks (⇨)
    • [ALGORITHM] Simple Baselines for Human Pose Estimation and Tracking (⇨)

Hand Models

  • Number of checkpoints: 23
  • Number of configs: 23
  • Number of papers: 6
    • [ALGORITHM] Deep High-Resolution Representation Learning for Visual Recognition (⇨)
    • [ALGORITHM] Deeppose: Human Pose Estimation via Deep Neural Networks (⇨)
    • [ALGORITHM] Distribution-Aware Coordinate Representation for Human Pose Estimation (⇨)
    • [ALGORITHM] Simple Baselines for Human Pose Estimation and Tracking (⇨)
    • [ALGORITHM] The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation (⇨)
    • [BACKBONE] Mobilenetv2: Inverted Residuals and Linear Bottlenecks (⇨)

Mesh Models

  • Number of checkpoints: 1
  • Number of configs: 1
  • Number of papers: 1
    • [ALGORITHM] End-to-End Recovery of Human Shape and Pose (⇨)

Top Down Models

  • Number of checkpoints: 123
  • Number of configs: 123
  • Number of papers: 20
    • [ALGORITHM] Albumentations: Fast and Flexible Image Augmentations (⇨)
    • [ALGORITHM] Convolutional Pose Machines (⇨)
    • [ALGORITHM] Deep High-Resolution Representation Learning for Human Pose Estimation (⇨ ⇨)
    • [ALGORITHM] Deeppose: Human Pose Estimation via Deep Neural Networks (⇨)
    • [ALGORITHM] Distribution-Aware Coordinate Representation for Human Pose Estimation (⇨)
    • [ALGORITHM] Improving Convolutional Networks With Self-Calibrated Convolutions (⇨)
    • [ALGORITHM] Learning Delicate Local Representations for Multi-Person Pose Estimation (⇨)
    • [ALGORITHM] Rethinking on Multi-Stage Networks for Human Pose Estimation (⇨)
    • [ALGORITHM] Simple Baselines for Human Pose Estimation and Tracking (⇨)
    • [ALGORITHM] Stacked Hourglass Networks for Human Pose Estimation (⇨)
    • [ALGORITHM] The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation (⇨)
    • [BACKBONE] Aggregated Residual Transformations for Deep Neural Networks (⇨)
    • [BACKBONE] Bag of Tricks for Image Classification With Convolutional Neural Networks (⇨)
    • [BACKBONE] Imagenet Classification With Deep Convolutional Neural Networks (⇨)
    • [BACKBONE] Mobilenetv2: Inverted Residuals and Linear Bottlenecks (⇨)
    • [BACKBONE] Resnest: Split-Attention Networks (⇨)
    • [BACKBONE] Shufflenet V2: Practical Guidelines for Efficient CNN Architecture Design (⇨)
    • [BACKBONE] Shufflenet: An Extremely Efficient Convolutional Neural Network for Mobile Devices (⇨)
    • [BACKBONE] Squeeze-and-Excitation Networks (⇨)
    • [BACKBONE] Very Deep Convolutional Networks for Large-Scale Image Recognition (⇨)

Whole-Body Models

  • Number of checkpoints: 12
  • Number of configs: 12
  • Number of papers: 3
    • [ALGORITHM] Deep High-Resolution Representation Learning for Human Pose Estimation (⇨)
    • [ALGORITHM] Distribution-Aware Coordinate Representation for Human Pose Estimation (⇨)
    • [ALGORITHM] Simple Baselines for Human Pose Estimation and Tracking (⇨)