Semantic Segmentation on Kitti Road Dataset using EfficientNet-B0 in PyTorch

An encoder-decoder model is used to perform semantic segmentation on Kitti Roaad Dataset in PyTorch. The encoder encodes the input images onto a low dimensional discriminative feature set and the decoder projects back the learnt features onto the high dimensional pixel space. The encoder uses a pre-trained EfficientNet B0 model and the decoder uses Transpose Convolutional layers to upsize the images back to its original size.

1. utils.py: set hyperparameters and file paths.
2. dataloader.py: load train and validation data into PyTorch DataLoader.
3. model.py: Define the encoder-decoder model.
4. train.py: Training procedure.

1. J. Fritsch, T. Kühnl and A. Geiger, "A new performance measure and evaluation benchmark for road detection algorithms," 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, 2013, pp. 1693-1700, doi: 10.1109/ITSC.2013.6728473.
2. M. Tan and Q. V Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks", arXiv:1905.11946
3. https://github.com/udacity/CarND-Semantic-Segmentation/
4. https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/