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lpsnet

Lightweight and Progressively-Scalable Networks for Semantic Segmentation

Reference

Zhang, Yiheng and Yao, Ting and Qiu, Zhaofan and Mei, Tao. "Lightweight and Progressively-Scalable Networks for Semantic Segmentation." arXiv preprint arXiv:2207.13600 (2022).

Performance

Cityscapes

Model Backbone Resolution Training Iters mIoU mIoU (flip) mIoU (ms+flip) Links
lpsnet_s - 1536x769 200000 71.73% 72.71% 73.61% model | log | vdl
lpsnet_m - 1536x769 200000 75.29% 76.03% 77.12% model | log | vdl
lpsnet_l - 1536x769 200000 75.72% 76.53% 77.50% model | log | vdl

Note that: Since the original paper does not provide all the implementation details, nor release the official training code, we first pre-trained the models on the ImageNet dataset, and then fine-tuned the models on the Cityscapes dataset. Specifically, compared with the experimental settings in the original paper, we made two significant changes during fine-tuning to obtain the above results:

  1. Extend iterations to 200k.
  2. Adjust the parameters used in color jittering.

For the pre-training results of the models on the ImageNet dataset, please see the ImageNet section, where we also have a discussion on the impact of the pre-trained model.

ImageNet

Model Epoch Top-1 accuracy Top-5 accuracy
lpsnet_s 120 0.403 0.666
lpsnet_m 100 0.543 0.786
lpsnet_l 120 0.525 0.773

Training settings are as following.

Optimizer

Optimizer LR Scheduler Regularizer
type: Momentum type: Piecewice type: L2
momentum: 0.9 lr: 0.1 coeff:0.0001
use_nesterov: true decay_epochs: [30, 60, 90] -
- values: [0.1, 0.01, 0.001, 0.0001] -

Data Augmentation

RandCropImage RandFlipImage Normalize
size: 224 flip horizontal ImageNet mean and std

With the pre-trained model, lpsnet_m is able to obtain 74.28% mIoU on Cityscapes val set under the same experimental settings as the original paper.

Further more, we also tried more complicated data augmentation strategies, e.g. random erasing and batched mix-up. The performance of classification improved by a considerable margin: on the ImageNet dataset, the top-1 accuracy increased to 0.564 and the top-5 accuracy increased to 0.805. However, we did not observe better segmentation performance of the model on the Cityscapes dataset using these pre-trained models. Therefore, these data augmentation strategies were not used.