• Lightweight and Progressively-Scalable Networks for Semantic Segmentation
• • Feature
  • Start
  • • Train
    • Evaluate
    • Test TIPC
  • Performance
  • • Cityscapes
    • ImageNet
  • Citation

Lightweight and Progressively-Scalable Networks for Semantic Segmentation

The Paddle Implementation of LPSNet based on PaddleSeg.

Feature

Compared to original code, this implemenation has the following advantages:

1. More streamlined.

2. More universal which is adaptable to any multi-path inputs.

3. Expandable.

   Start

   In order to make you focus on the key points, redundant files have been removed.

   LPSNet is defined in paddleseg/models/lpsnet.py

   Train

   Training with a single GPU.

   python train.py --config configs/lpsnet/lpsnet_m_cityscapes_1536x1024_200k.yml  --do_eval --use_vdl --log_iter 100 --save_interval 4000 --save_dir output
   

   Training with 4 GPUs.

   python -m paddle.distributed.launch train.py --config configs/lpsnet/lpsnet_m_cityscapes_1536x1024_200k.yml  --do_eval --use_vdl --log_iter 100 --save_interval 1000 --save_dir output
   

   Evaluate

   python val.py --config configs/lpsnet/lpsnet_m_cityscapes_1536x1024_200k.yml --model_path {your_model_path}
   

   Test TIPC

   Prepare dataset(very small)

   bash test_tipc/prepare.sh ./test_tipc/configs/lpsnet/train_infer_python.txt 'lite_train_lite_infer'
   

   Test TIPC.

   bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/lpsnet/train_infer_python.txt 'lite_train_lite_infer'
   

Performance

Cityscapes

Model	Backbone	Resolution	Training Iters	mIoU	mIoU (flip)	mIoU (ms+flip)	Links
lpsnet_s	-	1536x769	200000	71.73%	72.71%	73.76%	model | log | vdl
lpsnet_m	-	1536x769	200000	75.29%	76.03%	77.03%	model | log | vdl
lpsnet_l	-	1536x769	200000	75.72%	76.53%	77.23%	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.

Citation

@article{zhang2022lpsnet,
  title   = {Lightweight and Progressively-Scalable Networks for Semantic Segmentation},
  author  = {Zhang, Yiheng and Yao, Ting and Qiu, Zhaofan and Mei, Tao},
  journal = {ArXiv},
  year    = {2022},
  volume  = {abs/2207.13600}
}

@misc{liu2021paddleseg,
      title={PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation},
      author={Yi Liu and Lutao Chu and Guowei Chen and Zewu Wu and Zeyu Chen and Baohua Lai and Yuying Hao},
      year={2021},
      eprint={2101.06175},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{paddleseg2019,
    title={PaddleSeg, End-to-end image segmentation kit based on PaddlePaddle},
    author={PaddlePaddle Contributors},
    howpublished = {\url{https://github.com/PaddlePaddle/PaddleSeg}},
    year={2019}
}