FCN implementation for road segmentation on camvid dataset

• Pretrained vgg-16 encoder is used
• Decoder is trained [fcn-8, fcn-16, fcn-32] for road segmentation

References

• Camvid Dataset
• VGG for image classification
• FCN for semantic segmentation
• Loss based on dice similarity score - Dice loss for semantic segmentation
• FCN has also been used in MultiNet

To do

• Camvid dataloader
• Pretrained vgg-16 encoder
• Different losses
• Different FCN decoders
• Performance comparison

Performance comparison

model	loss	train accuracy	validation accuracy	test accuracy
fcn-8	cross-entropy	97.431	95.328	96.030
	dice-loss	97.471	95.762	96.204
	combined	97.554	95.454	96.159
fcn-16	cross-entropy	97.458	95.654	96.105
	dice-loss	97.456	95.439	96.110
	combined	97.509	95.685	96.199
fcn-32	cross-entropy	97.166	95.547	96.056
	dice-loss	97.141	95.401	95.784
	combined	97.182	95.449	95.947

• The metric used for comparison is pixel-wise accuracy score