# Inception-resnet-v2

**Repository Path**: jia0510/Inception-resnet-v2

## Basic Information

- **Project Name**: Inception-resnet-v2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-11-27
- **Last Updated**: 2021-11-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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