Overview¶
- Number of checkpoints: 223
- Number of configs: 223
- 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: 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