Metadata-Version: 2.1
Name: TorchSUL
Version: 0.1.12
Summary: Simple but useful layers for Pytorch
Home-page: https://github.com/ddddwee1/TorchSUL
Author: Cheng Yu
Author-email: chengyu996@gmail.com
License: UNKNOWN
Description: # TorchSUL
        
        This package is created for better experience while using Pytorch. 
        
        ## Why making this
        
        1. For fun.
        
        2. Path-dependence. I am addicted to my own wrap-ups. 
        
        3. Multi-platform. I have made the same APIs for pytorch, TF, MXNet, and a conversion tool to Caffe. 
        
        ## Installation
        
        You need to install the newest version of pytorch.
        
        Good, then just 
        
        ```
        pip install torchsul
        ```
        
        ## Projects 
        
        You can find some examples in the "example" folder.
        
        - ArcFace (Deng, Jiankang, et al. "Arcface: Additive angular margin loss for deep face recognition." arXiv preprint arXiv:1801.07698 (2018))
        
        - HR Net (Sun, Ke, et al. "Deep High-Resolution Representation Learning for Human Pose Estimation." arXiv preprint arXiv:1902.09212 (2019))
        
        - AutoDeepLab (Liu, Chenxi, et al. "Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019)
        
        - Knowledge distillation (Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. "Distilling the knowledge in a neural network." arXiv preprint arXiv:1503.02531 (2015))
        
        - 3DCNN (Ji, Shuiwang, et al. "3D convolutional neural networks for human action recognition." IEEE transactions on pattern analysis and machine intelligence 35.1 (2012): 221-231)
        
        - Temporal Convolutional Network (Not the same) (Pavllo, Dario, et al. "3D human pose estimation in video with temporal convolutions and semi-supervised training." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019)
        
        - Model conversions 
        
        - Batch_norm compression to speed-up models 
        
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
