ResNets.md

This is quick error-and-trial ResNet training attempts ImageNet-2012.

The architecture is similar to common ones for ImageNet, but has differences:

1. Images are resized to small side = 128 for speed reasons.
2. Networks are initialized with LSUV-init

Architectures, bad, overfitting

Except GoogLeNet

Name	Accuracy	LogLoss	Comments
ThinResNet-101	0.407	2.80	3x thinner, than in paperWith BN, huge overfitting, no dropout. First attempt to train, probably smth went wrong
ThinResNet-101	0.518	2.16	Without BN, less overfitting (still > than HeNet), no dropout. Looks like needs bigger initial LR, than caffenet. 3rd attempt is on-going
ThinResNet-101	0.525	2.12	Without BN, less overfitting (still > than HeNet), no dropout. LR=0.05 until 25K iters.
ThinResNet-101	0.567	1.91	Without BN, ELU, linear lr_policy.
ResNet-56BN	0.612	1.70	With BN, no EA, step lr_policy. Stopped at 220K iters due to overfitting
ResNet-56ELU bad :(	0.515	2.43	Double stride after maxpool1 by mistake :( Without BN, ELU, step lr_policy. Stopped at 220K iters due to overfitting
DereResNet-45	0.495	2.25	Double stride after maxpool1 by mistake :( With BN, no EA, dereyly lr_policy. Stopped at 120K iters due to overfitting
GoogLeNet-128	0.619	1.61	For reference. linear lr_policy, batch_size=256.

Architectures, good attempts

Name	Accuracy	LogLoss	Comments
ResNet-50ELU-2xThinner	0.616	1.63	Without BN, ELU, dropout=0.2 before classifier. 2x thinner, than in paper. Quite fast. No large overfitting (unlike upper table)
GoogLeNet-128	0.619	1.61	For reference. linear lr_policy, batch_size=256.