universome / visum

VISUM 2019 object detection challenge

TODO

• Gradient accumulation
• Albumentations
• Leave-one-class-out cross-validation
• Better backbone
• freeze backbone for training to run faster
• crop objects from some examples and paste them to other images
• class dropout, train "any-class" model msh
• label smoothing msh
• check why new class prediction does not work: if it RPN or classification
• Reduce images, because large part of an image is not useful at all
• Try single-stage detectors
• Visualize predictions and train data (we would like to see what part of an image can be cropped out)
• TensorboardX: training progress, learning rate, val metrics, etc
• Test-time augmentation
• Normal ensembling; ensembling models from previous epochs
• Add grayscale IR images to train procedure
• Check wtf is with the books, why we do not predict them
• Generate visualizations after training
• Train on the whole train set for final submission
• Check that bbox sizes of Faster-RCNN fit our case