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GluonCV 0.9.0 Release
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@bryanyzhu bryanyzhu released this 02 Dec 22:25
· 88 commits to master since this release
v0.9.0
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Highlights

GluonCV v0.9.0 starts to support PyTorch!

PyTorch Support

We want to make our toolkit agnostic to deep learning frameworks so that it is available for everyone. From this release, we start to support PyTorch. All PyTorch code and models are under torch folder inside gluoncv, arranged in the same hierarchy as before: model, data, nn and utils. model folder contains our model zoo with model definitions, data folder contains dataset definition and dataloader, nn defines new operators and utils provide utility functions to help model training, evaluation and visualization.

To get started, you can find installation instructions, model zoo and tutorials on our website. In order to make our toolkit easier to use and customize, we provide model definitions separately for each method without extreme abstraction and modularization. In this manner, you can play with each model without jumping across multiple files, and you can modify individual model implementation without affecting other models. At the same time, we adopt yaml for easier configuration. We thrive to make our toolkit more user friendly for students and researchers.

Video Action Recognition PyTorch Model Zoo

We have 46 PyTorch models for video action recognition, with better I3D models, more recent TPN family, faster training (DDP support and multi-grid) and K700 pretrained weights. Finetuning and feature extraction can never be easier.

Details of our model zoo can be seen at here. In terms of models, we cover TSN, I3D, I3D_slow, R2+1D, Non-local, CSN, TSN and TPN. In terms of datasets, we cover Kinetics400, Kinetics700 and Something-something-v2. All of our models have similar or better performance compared to numbers reported in original paper.

We provide several tutorials to get you started, including how to make predictions using a pretrained model, how to extract video features from a pretrained model, how to finetune a model on your dataset, how to measure a model's flops/speed, and how to use our DDP framework.

Since video models are slow to train (due to slow IO and large model), we also support distributed dataparallel (DDP) training and multi-grid training. DDP can provide 2x speed up and multi-grid training can provide 3-4x speed up. Combining these two techniques can significantly shorten the training process. In addition, both techniques are provided as helper functions. You can easily add your model definitions to GluonCV (a single python file like this) and enjoy the speed brought by our framework. More details can be read in this tutorial.

Bug fixes and Improvements

  • Refactored table in csv form. (#1465 )
  • Added DeepLab ResNeSt200 pretrained weights (#1456 )
  • StyleGAN training instructions (#1446 )
  • More settings for Monodepth2 and bug fix (#1459 #1472 )
  • Fix RCNN target generator (#1508)
  • Revise DANet (#1507 )
  • New docker image is added which is ready for GluonCV applications and developments(#1474)

Acknowledgement

Special thanks to @Arthurlxy @ECHO960 @zhreshold @yinweisu for their support in this release. Thanks to @coocoo90 for contributing the CSN and R2+1D models. And thanks to other contributors for the bug fixes and improvements.

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