Inception-resnet-v2 Test

============================

Original Paper: "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning"(https://arxiv.org/abs/1602.07261) from Google

Notes

Data augmentation applied (please find the data augmentation fork in https://github.com/twtygqyy/caffe-augmentation):

max_color_shift = 5

contrast_variation = 0.8 ~ 1.2

max_brightness_shift = 5 

mirror = true

min_side = 328 ~ 480 and crop by 299x299 for training, min_side = 328 and crop by 299x299 for testing

init learning rate = 0.072 with RMSProp optimizer (rms_decay = 0.9 delta = 0.9)

max_iter = 1066080

stepsize = 6663

gamma = 0.94

weight_decay = 0.0004

clip_gradients = 80

4 Geforce 1080 GPU are used for training and batch size = 5 x 4 (Very huge memory required for training)

Result

Test net output #0: accuracy_top1 = 0.729467

Test net output #1: accuracy_top5 = 0.904265

Model link: https://drive.google.com/file/d/0B5i4atpKg9EcOGRqUExXZVNxODQ/view?usp=sharing

Different solver with more iterations is under training right now