code for our paper "Renovate Yourself: Calibrating Feature Representation of Misclassified Pixels for Semantic Segmentation"
the corresponding article
| Network | Encoder | Iteration | Train | Test | \rho | \tau | mIoU(log) | pth | comments |
|---|---|---|---|---|---|---|---|---|---|
| OCRNet | HRNet-W48 | 40000 | train set | val set | 0.9 | 0.5 | 82.24 | pth | |
| OCRNet | HRNet-W48 | 40000 | train set | val set | 0.9 | 0.5 | 82.29 | pth | Reproduced |
| OCRNet | HRNet-W48 | 40000 | train set | val set | 0.8 | 0.5 | 82.25 | _ | |
| OCRNet | HRNet-W48 | 40000 | train set | val set | 0.9 | 0.25 | 81.88 | _ | |
| FCN | HRNet-W48 | 40000 | train set | val set | 0.9 | 0.25 | 81.83 | pth |
python>=3.6
torch>=1.7
torchvision
visdom
numpy
pillow
scikit-learn
For the GPUs, we use 8 NVIDIA TITAN X(P) GPUs.
- core code of RCH is ./mmseg/models/decode_heads/decode_head_new.py
- Please replace your dataset dir into the corresponding config file.
- train OCRNet on cityscapes:
./tools/dist_train.sh ./configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py 8
Now, you can use RCH to train OCRNet, FCN, DeepLab. We will continue to update more available decoders。
@InProceedings{wang2021rch,
tilte = {Renovate Yourself: Calibrating Feature Representation of Misclassified Pixels for Semantic Segmentation},
author = {Wang, Hualiang and Chu, Huanpeng and Fu, siming},
booktitle = {AAAI},
year = {2022}
}