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A PyTorch implementation for ERFNet on Pascal VOC data.

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ERFNET-PyTorch

A PyTorch implementation for ERFNet, for Semantic Segmentation on Pascal VOC.

Table of Contents:

Project Structure:

├── agents
|  └── erfnet.py # the main training agent 
├── graphs
|  └── models
|  |  └── erfnet.py  # model definition for semantic segmentation
|  |  └── erfnet_imagenet.py  # model definition for imagenet
|  └── losses
|  |  └── loss.py # contains the cross entropy 
├── datasets  # contains all dataloaders for the project
|  └── voc2012.py # dataloader for Pascal Voc dataset
├── data
├── utils # utilities folder containing metrics , config parsing, etc
|  └── assets
├── main.py
├── run.sh

Data Preparation:

We used Pascal Voc 2012 data; to prepare them for training, do the following:

  • Visit this, download SBD and PASCAL VOC 2012
  • Extract them, you will get benchmark_RELEASE and VOCdevkit folders.
  • Download seg11valid.txt and add it into VOCdevkit/VOC2012/ImageSets/Segmentation
  • Put the benchmark_RELEASE and VOCdevkit folders in a folder named VOC.
  • Remove the color map in ground truth annotations by using remove_gt_colormap.py
  • Then Check ./utils/voc_utils.py and ./datasets/voc2012.py for iterating on the VOC Dataset.
  • Note that all images has been resized to (256x256) for consistency.

Model:

We are using the same model architecture as given in the paper.

alt text

Experiment Configs:

We used the pretrained model on imagenet for ERFNet encoder and trained the model on Pascal VOC for 70 epochs only.

- Input size: 256x256x3
- Batch size: 64
- Learning rate: 5e-4
- learning_rate_patience: 100
- Betas for Adam: 0.5 and 0.999
- Number of epochs: 70

Usage:

  • To run the project, you need to add your configurations into the folder configs/. An example of the configurations that should be passed can be found here
  • sh run.sh
  • To run on a GPU, you need to enable cuda in the config file.

Results:

We only ran a single experiment and got the following results:

Metric Reproduced
Accuracy 88.75%
Mean-IOU 55.50%

Segmented Images after training:

alt text alt text alt text alt text alt text alt text alt text alt text

Requirements:

  • Pytorch: 0.4.0
  • torchvision: 0.2.1
  • tensorboardX: 1.2

Check requirements.txt.

Future Work:

  • Train ERFNet with weighted loss

References:

License:

This project is licensed under MIT License - see the LICENSE file for details.

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