Design strategies for higher resolution images for Convolutional neural network?
By BVLC/Caffe examples cifar10, it has 3 convolution layers with pool/norm and its accuracy increases to around 82 easily. Now I want to increase my resolution by downloading the 10 classes of images from imagenet and make the image resolution to 55.
The scenario is that the images have higher resolution and less data: 5000 per-class while cifar-10 has 6000 per class. What are good design strategies for this new scenario? Is the performance going to drop a lot?
Topic convolutional-neural-network caffe deep-learning
Category Data Science