Tiny Imagenet

This project follows the assignment 2. of Practical Machine Learning.

Dependencies

• tensorflow >= 1.4
• numpy

Setup

1. Download training dataset at tiny-imagenet and put it on the data directory.
2. Download a pretrained VGG16 Model from http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz, extract and put vgg16.ckpt on the pretrained directory.
3. (Optional) For evaluating the assignment test files, download it at http://www.suyongeum.com/ML/assignments.php and put it on data/test.
4. Please run the train.py or predict.py

Model

I used the VGG-16 Model and show the architectures below in detail.

Architecture

1. input layer: output=64x64x3
2. conv1 layer: output=64x64x64, ksize=[3, 3], stride=1
3. pool1 layer: output=32x32x64, func=max, ksize=[2, 2]
4. conv2 layer: output=32x32x128, ksize=[3, 3], stride=1
5. pool2 layer: output=16x16x128, func=max, ksize=[2, 2]
6. conv3 layer: output=16x16x256, ksize=[3, 3], stride=1
7. pool3 layer: output=8x8x256, func=max, ksize=[2, 2]
8. conv4 layer: output=8x8x512, ksize=[3, 3], stride=1
9. pool4 layer: output=4x4x512, func=max, ksize=[2, 2]
10. conv5 layer: output=4x4x512, ksize=[3, 3], stride=1
11. pool5 layer: output=2x2x512, func=max, ksize=[2, 2]
12. fc1 layer: output=512
13. fc2 layer: output200

Settings

• optimization: adam(lr=0.01)
• batch size: 500
• epoch: 10000
• regularization: weight decay(add L2 norm into loss)
• all activation function: ReLU
• data augumentation:
  • brightness
  • contrast
  • saturation
  • crop
  • flip the image left or right
  • normalization
• To use a prepared pretrained model before training

Results