README.md

Abstract

• Instruction

  We search efficient E3D backbones for action recognition and E3D-S/M/L are aligned with X3D-S/M/L.
  

• Use the searching configs for Classification

  sh tools/dist_search.sh configs/E3D_X3DS_FLOPs.py

  E3D_X3DS_FLOPs.py is the config for searching X3DS-like model within the budget of FLOPs using STEntr Score.

  E3D_X3DM_FLOPs.py is the config for searching X3DM-like model within the budget of FLOPs using STEntr Score.

  E3D_X3DL_FLOPs.py is the config for searching X3DL-like model within the budget of FLOPs using STEntr Score.

• Use searched models in your own training pipeline

  copy tinynas/deploy/cnn3dnet to your pipeline, then

  from cnn3dnet import Cnn3DNet
  # for classifictaion
  model = Cnn3DNet(num_classes=classes, 
                  structure_txt=structure_txt,
                  out_indices=(4,),
                  classfication=True)
  
  # if load with pretrained model
  model.init_weights(pretrained=pretrained_pth)

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Results on Sth-Sth V1

Backbone	size	FLOPs (G)	Top-1	Top-5	Structure
E3D-S	160	1.9	47.1	75.6	txt
E3D-M	224	4.7	49.4	78.1	txt
E3D-L	312	18.3	51.1	78.7	txt

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Citation

If you find this toolbox useful, please support us by citing this work as

@inproceedings{iclr23maxste,
	title     = {Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition},
	author    = {Junyan Wang and Zhenhong Sun and Yichen Qian and Dong Gong and Xiuyu Sun and Ming Lin and Maurice Pagnucco and Yang Song },
	journal   = {International Conference on Learning Representations},
	year      = {2023},
}