Dataset_size.md

This is quick evaluation of trainig set size impact on performance on ImageNet-2012.

The architecture is similar to CaffeNet, but has differences:

1. Images are resized to small side = 128 for speed reasons.
2. fc6 and fc7 layers have 2048 neurons instead of 4096.
3. Networks are initialized with LSUV-init
4. No LRN layers.

Default augmentation: random crop 128x128 from 144xN image, 50% random horizontal flip.

Dataset size

Name	Accuracy	LogLoss	Comments
Default, 1.2M images	0.471	2.36
800K images	0.438	2.54
600K images	0.425	2.63
400K images	0.393	2.92
200K images	0.305	4.04

Dataset size, no RGB scaling

Or why input var=1 for LSUV is so important

Name	Accuracy	LogLoss	Comments
800K images	0.438	2.54
600K images	0.425	2.63
600K images, no scale	0.379	2.92
400K images	0.393	2.92
400K images, no scale	0.357	3.10
200K images	0.305	4.04
200K images, no scale	0.277	4.06

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