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).
| 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:
- Extend iterations to 200k.
- 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.
| 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.