Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation

Introduction

This is the official code of

Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation (AAAI-2022 Oral).

Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks (under review)

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Data preparation

You need to download the ER and MITO datasets.

Your directory tree should be look like this:

$SEG_ROOT/datasets
├── er
│   ├── test
│   │   ├── images
│   │   ├── labels
│   ├── train
│   │   ├── images
│   │   ├── labels
│   ├── val
│   │   ├── images
│   │   ├── labels
├── mito
│   ├── test
│   │   ├── images
│   │   ├── labels
│   ├── train
│   │   ├── images
│   │   ├── labels
│   ├── val
│   │   ├── images
│   │   ├── labels
├── txt
│   ├── er
│   │   ├── train
│   │   │   ├── train_gt.txt
│   │   │   ├── train_noisyLabel.txt
│   │   └── val.txt
│   ├── mito
│   │   ├── train
│   │   │   ├── train_gt.txt
│   │   │   ├── train_noisyLabel.txt
│   │   └── val.txt

Training

To train model, you should save the datapath into a __.txt file and put it into the txt dictionary, then run main.py for training.

Testing

To test the segmentation performance, you should first run evaluation/inference.py to save the outputs of testing sets in train_log (Use parameters train_dir and test_ckpt_epoch to change the path of pre-trained models).

Then, you can run evaluation/inference.py to get different metrics scores such as IOU, F1 and others on testing set. (Use parameter test_data_dir to change the testing datapath __.txt. Use parameter prd_dir to change the saved predictions path of testing sets).

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Reference

[1] Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation. Yaoru Luo, Guole Liu, Yuanhao Guo, Ge Yang Accepted by AAAI-22. download

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Contributing

Code for this projects developped at CBMI Group (Computational Biology and Machine Intelligence Group).

CBMI at National Laboratory of Pattern Recognition, INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES.