Welcome to MMPose’s documentation!¶
Datasets
Model Zoo
- Top Down Models
- Imagenet classification with deep convolutional neural networks
- Convolutional pose machines
- Distribution-aware coordinate representation for human pose estimation
- Stacked hourglass networks for human pose estimation
- Deep high-resolution representation learning for human pose estimation
- Mobilenetv2: Inverted residuals and linear bottlenecks
- Simple baselines for human pose estimation and tracking
- ResNetV1D
- Aggregated residual transformations for deep neural networks
- Improving Convolutional Networks with Self-Calibrated Convolutions
- Squeeze-and-excitation networks
- Shufflenet: An extremely efficient convolutional neural network for mobile devices
- Shufflenet v2: Practical guidelines for efficient cnn architecture design
- Bottom Up Models
- Pretrained backbones on ImageNet