pytorch-fcn

Fully Convolutional Networks implemented with PyTorch.

TODO

• Support FCN16s and FCN8s.

Accuracy

FCN32s

• deconv=False
• train=SBDClassSeg(split='train')
• val=VOC2011(split='seg11val')
• batch_size=1
• MomentumSGD(lr=1e-10, momentum=0.99, weight_decay=0.0005)

epoch	iteration	valid/loss	valid/acc	valid/acc_cls	valid/mean_iu	valid/fwavacc
9	76482	59656.847812	0.897753	0.780288	0.628707	0.844420

Speed

It is ~4 times faster than FCN implemented with Chainer, measuring on Titan X Pascal.

% ./speedtest.py --gpu 0 --times 1000
==> Running on GPU: 0 to evaluate 1000 times
==> Testing FCN32s with Chainer
Elapsed time: 208.34 [s / 1000 evals]
Hz: 4.80 [hz]
==> Testing FCN32s with PyTorch
Elapsed time: 56.30 [s / 1000 evals]
Hz: 17.76 [hz]