Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Related tags

Deep Learning NRD_decoder
Overview

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Requirements

This repository needs mmsegmentation

Training

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

Evaluation

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

Pre-trained Models

Results

Our model achieves the following performance on :

[Semantic segmentation results]

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) 40.9 (60 classes)
NRD-r50 Cityscapes (val) 79.8 80.8
NRD-r101 Cityscapes (val) 80.7 82.0

Contributing

The code is mostly taken from mmsegmentation mmsegmentation is released under the Apache 2.0 license.

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