https://gist.github.com/jimgoo/0179e52305ca768a601f

The is the reference CaffeNet (modified AlexNet) fine-tuned for the Oxford 102 category flower dataset. The number of outputs in the inner product layer has been set to 102 to reflect the number of flower categories. Hyperparameter choices reflect those in Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data. The global learning rate is reduced while the learning rate for the final fully connected is increased relative to the other layers.

After 50,000 iterations, the top-1 error is 7% on the test set of 1,020 images.

<br/>I0215 15:28:06.417726 6585 solver.cpp:246] Iteration 50000, loss = 0.000120038<br/>I0215 15:28:06.417789 6585 solver.cpp:264] Iteration 50000, Testing net (#0)<br/>I0215 15:28:30.834987 6585 solver.cpp:315] Test net output #0: accuracy = 0.9326<br/>I0215 15:28:30.835072 6585 solver.cpp:251] Optimization Done.<br/>I0215 15:28:30.835083 6585 caffe.cpp:121] Optimization Done.<br/>

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