Awesome

ERFNET-PyTorch

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

├── 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.