This repository needs mmsegmentation
To train the model(s) in the paper, run this command:
python tools/train.py ./configs/NRD/ade20k/NRD_r101_512x512_164k_ade20k.py
The batch size is 16 in this work. Please change the 'samples_per_gpu' in configs/base/datasets/.. accordingly
To evaluate my model at single-scale inference, run:
python tools/eval.py ./configs/NRD/ade20k/NRD_r101_512x512_164k_ade20k.py {path-to-checkpoint-file} --eval mIoU
Our model achieves the following performance on :
| Model name | datasets | mIoU | mIoU (ms) |
|---|---|---|---|
| NRD-r101 | ade20k (val) | 44.01 | 45.62 |
| NRD-x101 | ade20k (val) | 44.34 | 46.35 |
| NRD-r101 | pascal-context(val) | 52.31 (59 classes) | 54.1 (59 classes) |
| NRD-r101 | pascal-context(val) | 47.5 (60 classes) | 49.0 (60 classes) |
| NRD-r50 | Cityscapes (val) | 79.8 | 80.8 |
| NRD-r101 | Cityscapes (val) | 80.7 | 82.0 |
The code is mostly taken from mmsegmentation mmsegmentation is released under the Apache 2.0 license.