# Fine tune avg-vgg16 on CUB200 dataset
Remove fc layers, replace pool5 with 14x14 average pooling and softmax layer, then fine tune on CUB200.

## How to use
* modify generate_prototxt.py to specify the path of caffe, lmdb
* ./fine_tune.sh

## Implementation details
* lmdb: 256*256 cub200 dataset(#train: 5994, #val:5794).
* adopt random cropping to generate 224*224 images when training and use center cropping when test.
* training issues:
	* learning rate of softmax is 10x larger;
	* batch size:32, 188 iters/epoch, train 21 epoch;
	* use 2 gpu cards;
	* learning rate change 10^-3~10^-5 (decay 10x every 7 epoch);

## Results
| epoch | val acc | epoch | val acc | epoch | val acc |
| ----- | --------| ----- | --------| ----- | --------|
|	1	|	0.4294 	|	2	|	0.5299 	|	3	|	0.5614 	|
|	4	|	0.5740 	|	5	|	0.6051 	|	6	|	0.5564 	|
|	7	|	0.5732 	|	8	|	0.7083 	|	9	|	0.7130 	|
|	10	|	0.7154 	|	11	|	0.7106 	|	12	|	0.7125 	|
|	13	|	0.7126 	|	14	|	0.7140 	|	15	|	0.7145 	|
|	16	|	0.7149 	|	17	|	0.7147 	|	18	|	0.7145 	|
|	19	|	0.7132 	|	20	|	0.7119 	|	21	|	0.7118 	|

## Others
This strategy is much better than train softmax first follwed with fine tuning, which only achieving 0.70176 accuracy!