Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Related tags

Deep LearningNRD_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.

Owner
Adelaide Intelligent Machines (AIM) Group
Adelaide Intelligent Machines (AIM) Group
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