A pytorch codebase for human parsing and vehicle parsing
A pytorch codebase for human parsing and vehicle parsing.
A pytorch codebase for human parsing and vehicle parsing. The introduction of our new MVP dataset for vehicle parsing can be found HERE.
pip install tensorboard
The scripts to train and test models are in train_test
.
The scripts for PSPNet, DeepLabV3, and HRNet are ready for directly running.
The train/val/test splitting files used in our experiments can be found here.
Models trained on the MVP dataset for vehicle parsing:
Method | Dataset | Pixel Acc | Mean Acc | mIoU | download |
---|---|---|---|---|---|
PSPNet | MVP-Coarse | 90.26% | 89.08% | 79.78% | model |
PSPNet | MVP-Fine | 86.21% | 69.61% | 57.47% | model |
DeepLabV3 | MVP-Coarse | 90.55% | 89.45% | 80.41% | model |
DeepLabV3 | MVP-Fine | 87.42% | 73.50% | 61.60% | model |
HRNet | MVP-Coarse | 90.40% | 89.36% | 80.04% | model |
HRNet | MVP-Fine | 86.47% | 72.62% | 60.21% | model |
* The performance is evaluated on the test set.
** The PSPNet and HRNet models are trained with cross-entropy loss. The DeepLabV3 models are trained with cross-entropy + IoU loss.
*** We also released several pre-trained model on the LIP dataset. Please refer to models.
@inproceedings{mm/LiuZLSM19,
author = {Xinchen Liu and
Meng Zhang and
Wu Liu and
Jingkuan Song and
Tao Mei},
title = {BraidNet: Braiding Semantics and Details for Accurate Human Parsing},
booktitle = ACM MM,
pages = {338--346},
year = {2019}
}
@inproceedings{mm/LiuLZY020,
author = {Xinchen Liu and
Wu Liu and
Jinkai Zheng and
Chenggang Yan and
Tao Mei},
title = {Beyond the Parts: Learning Multi-view Cross-part Correlation for Vehicle
Re-identification},
booktitle = {ACM MM},
pages = {907--915},
year = {2020}
}