This repository contais the implementation of Semantic Segmentation Deep Learning architectures for the Computer Vision course. For more info about the course, please refer to its website (in Portuguese).
The following segmentation models are currently made available:
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Encoder-Decoder based on SegNet. This fully convolutional network uses a VGG-style encoder-decoder, where the upsampling in the decoder is done using transposed convolutions.
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Encoder-Decoder with skip connections based on SegNet. This fully convolutional network uses a VGG-style encoder-decoder, where the upsampling in the decoder is done using transposed convolutions. Also, it employs additive skip connections from the encoder to the decoder.
- The class segmentation: pixel indices correspond to classes in alphabetical order (1=aeroplane, 2=bicycle, 3=bird, 4=boat, 5=bottle, 6=bus, 7=car , 8=cat, 9=chair, 10=cow, 11=diningtable, 12=dog, 13=horse, 14=motorbike, 15=person, 16=potted plant, 17=sheep, 18=sofa, 19=train, 20=tv/monitor). The index 0 corresponds to background.
