Deep neural nets like DenseNet or ResNet are good for computer vision even for moderate dataset (like this competition: ~45000)

While stochastic gradient descent with momentum needs more time to converge, with a good learning rate setup, it is able to outperform Adam. (why??)

Fine-tuning pre-trained deep model usually works better than training from scratch. (why??)

Ensemble method is a good way to increase accuracy (maybe different models learn different features, by averaging these differences, it reduces biases and variances.)

Finish feature pyramid network with group convolution (Xception-like network?) and train it on a smaller sized image. (128 * 128?)

Train ResNet-152 and do ensemble with DenseNets

Use the IR channel.