Hi everyone,
I want to realize a ship detector based on neural network. It is the first time that i try to use neural network for object detection therefore my question may be trivial. My idea is to use transfer learning on Alexnet and than to train the detector with this network.
DATASET:
- How many images a need in order to train perform the transfer learning on Alexnet?
- Should the detector be able to recognize ships seen from every point of view (if images with multiple point of view are included in the data-set) or it is necessary to use multiple detector to identify different ship observed from different orientations (HOG detector style)?
- Should the images contain only the object to be detected (ships in this case) or the entire maritime scene with the ship highlighted by a boundig-box?
- I found online two or three data set that correspond at the descriprion of the first case above, but anyone for the second case. Any link suggestion for the second case? (that is the one that I think is correct)
OPTIONS:
I found multiple type of options navigating in the web, the most common used are:
opts = trainingOptions('sgdm', ... 'Momentum', 0.9, ... 'InitialLearnRate', 0.001, ... 'LearnRateSchedule', 'piecewise', ... 'LearnRateDropFactor', 0.1, ... 'LearnRateDropPeriod', 8, ... 'L2Regularization', 0.001, ... 'MaxEpochs', 100, ... 'MiniBatchSize', 256, ... 'Verbose', true);
How can i relate the MiniBatchSize to the amount of images that I have as dataset?
Thank you!
Best Answer