Comments (1)
Training of all classes at once is fine and it is usually done this way in real tasks. It was competition settings and severe class imbalance which forced us to treat each class independently. Also, there are some research that shows that usually networks trained on harder tasks work better in simple cases. So, I'd suggest training all at once (and use pretrained network, always)
from kaggle_dstl_submission.
Related Issues (20)
- Using real coordinates
- h5py.File doesn't have compression as argument? HOT 1
- output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None HOT 3
- ValueError: "concat" mode can only merge layers with matching output shapes except for the concat axis. Layer shapes: [(None, 1024, 14, 7), (None, 256, 14, 14)] HOT 2
- RGB implementation HOT 4
- Object detection question HOT 1
- TypeError: 'compression_opts' is an invalid keyword argument for this function(cache_train.py) HOT 1
- Running visualize error HOT 1
- How can I label my own images? HOT 1
- Thanks so much HOT 1
- How can I make this code work with images of 3 bands only? HOT 3
- why loss go up?
- model issue
- TypeError: 'compression' is an invalid keyword argument for this function HOT 2
- ValueError: "concat" mode can only merge layers with matching output shapes except for the concat axis. Layer shapes: [(None, 2, 14, 512), (None, 2, 14, 256)] HOT 7
- Reproducing Results Problem HOT 1
- make prediction on buildings is failing HOT 5
- Running code only with RGB images HOT 3
- Dimension of the traning set images HOT 1
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