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GoogLeNet GPU implementation from Princeton.

2019-09-20 | |  105 |   0 |   0

We implemented GoogLeNet using a single GPU. Our main contribution is an effective way to initialize the network and a trick to overcome the GPU memory constraint by accumulating gradients over two training iterations.
* Please check http://3dvision.princeton.edu/pvt/GoogLeNet/ for more information. Pre-trained models on ImageNet and Places, and the training code are available for download.
* Make sure cls2_fc2 and cls3_fc have num_output = 1000 in the prototxt. Otherwise, the trained model would crash on test.

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上一篇：Places-CNN model from MIT.

下一篇：CaffeNet fine-tuned for Oxford flowers dataset

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