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README.md

Group Whitening

This project provides the PyTorch implementation of Group Whitening described in the following paper:

Group Whitening: Balancing Learning Efficiency and Representational Capacity, arXiv:2009.13333.

Highlights

  • We believe the proposed Group Whitening (GW) module is a practical component, the implementation of which is in the directory: ./classification/extension/normalization/.
  • GW consistently improves the performance of ResNet and ResNeXt, with absolute gains of 1.02% ∼ 1.49% in top-1 accuracy on ImageNet (repo: ./classification/) and 1.82% ∼ 3.21% in bounding box AP on COCO object detection (repo: /maskrcnn_debug/).

Table 1, ImageNet Classification. Standard setup: batchSize=256, wd=0.0001, init lr=0.1, 100 epochs (30,60,90- decay).

BaseLine Using GW module
ResNet-50 76.23 77.72 (model, pth )
ResNet-101 77.69 78.71 (model, pth )
ResNeXt-50 77.01 78.43 (model, pth )
ResNeXt-101 79.29 80.43 (model, pth )

Table 2, COCO object detection results using Faster R-CNN with ResNet-50+FPN. We use the 1x lr scheduling (90k iterations), with a batch size of 16 on 8 GPUs.

2fc head box 4conv 1fc head box
BN-frozen 36.31% AP 36.39 % AP
GN 36.32% AP 37.86 % AP
GW 38.13% AP 39.60 % AP

Table 3, COCO object detection and segmentation results using Mask R-CNN with ResNeXt-101+FPN. We use the 1x lr scheduling (180k iterations), with a batch size of 8 on 8 GPUs.

AP (box) AP(mask)
BN-frozen 42.24% 37.53%
GN 42.18% 37.54%
GW 44.41% 39.17%

Contact

huanglei36060520@gmail.com

Acknowledgement

Note that the code repo ./classification/ is based on the IterNorm project, and the code repo /maskrcnn_debug/is based on the maskrcnn project.

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