README.md

SegNet

SegNet is a TensorFlow implementation of the segmentation network proposed by Kendall et al., with cool features like strided deconvolution, a minified architecture and more.

Configuration

Create a config.py file, containing color maps, working dataset and other options.

autoencoder = 'segnet'
colors = {
  'segnet-32': [
    [64, 128, 64],   # Animal
    [192, 0, 128],   # Archway
    [0, 128, 192],   # Bicyclist
    [0, 128, 64],    # Bridge
    [128, 0, 0],     # Building
    [64, 0, 128],    # Car
    [64, 0, 192],    # CartLuggagePram
    [192, 128, 64],  # Child
    [192, 192, 128], # Column_Pole
    [64, 64, 128],   # Fence
    [128, 0, 192],   # LaneMkgsDriv
    [192, 0, 64],    # LaneMkgsNonDriv
    [128, 128, 64],  # Misc_Text
    [192, 0, 192],   # MotorcycleScooter
    [128, 64, 64],   # OtherMoving
    [64, 192, 128],  # ParkingBlock
    [64, 64, 0],     # Pedestrian
    [128, 64, 128],  # Road
    [128, 128, 192], # RoadShoulder
    [0, 0, 192],     # Sidewalk
    [192, 128, 128], # SignSymbol
    [128, 128, 128], # Sky
    [64, 128, 192],  # SUVPickupTruck
    [0, 0, 64],      # TrafficCone
    [0, 64, 64],     # TrafficLight
    [192, 64, 128],  # Train
    [128, 128, 0],   # Tree
    [192, 128, 192], # Truck_Bus
    [64, 0, 64],     # Tunnel
    [192, 192, 0],   # VegetationMisc
    [0, 0, 0],       # Void
    [64, 192, 0]     # Wall
  ]
}
gpu_memory_fraction = 0.7
strided = True
working_dataset = 'segnet-32'

Two kinds of architectures are supported at the moment: the original SegNet Encoder-Decoder (segnet), and a smaller version of the same (mini), which can be used for simpler segmentation problems. I suggest to use strided = True for faster and more reliable results.

The dataset_name needs to match the data directories you create in your input folder. You can use segnet-32 and segnet-13 to replicate the aforementioned Kendall et al. experiments.

Train and test

Generate your TFRecords using tfrecorder.py. In order to do so, put your PNG images in a raw folder, as follows:

input/
    raw/
        train/
        train-labels/
        test/
        test-labels/

Once you have your TFRecords, train SegNet with python src/train.py. Analogously, test it with python src/test.py.