The programs in this repository train and use a fully convolutional neural network to take an image and classify its pixels. The network is transfer-trained basing on the VGG-16 model using the approach described in this paper by Jonathan Long et al. The software is generic and easily extendable to any dataset, although I only tried with KITTI Road Dataset and Cityscapes dataset so far. All you need to do to introduce a new dataset is to create a new source_xxxxxx.py file defining your dataset. The definition is a class that contains seven attributes:

• image_size - self-evident, both horizontal and vertical dimention need to be divisible by 32
• num_classes - number of classes that the model is supposed to handle
• label_colors - a dictionary mapping a class number to a color; used for blending of the classification results with input image
• num_training - number of training samples
• num_validation - number of validation samples
• train_generator - a generator producing training batches
• valid_generator - a generator producing validation batches

See source_kitti.py or source_cityscapes.py for a concrete example. The trainer picks the source based on the value of the --data-source parameter.

Training the model on the KITTI Road Dataset essentially means that infer.py will be able to take images from a car's dashcam and paint the road pink. It generalizes fairly well even to pretty complicated cases:

The model that produced the above images was trained for 500 epochs on the images contained in this zip file. The training program fills tensorboard with the loss summary and a sneak peek of the current performance on validation examples. The top row contains the ground truth and the bottom one the network's output.

This dataset is more complex than the previous one. It has fine image annotations for 29 classes of objects. The images are video frames taken in German cities and there is around 11GB of them.

The model that produced the images was trained for 150 epochs.