Overview¶
Number of checkpoints: 229
Number of configs: 230
Number of papers: 26
ALGORITHM: 16
BACKBONE: 10
For supported datasets, see datasets overview.
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 (⇨)
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: 124
Number of configs: 125
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