Amazon_Forest_Computer

Composing data augmentations, also here. Note use Pillow-SIMD instead of PIL/Pillow. It is even faster than OpenCV

Loading from a CSV that contains image path - 61 lines yeah

Equivalent in Keras - 216 lines ugh. Note: so much lines were needed because by default in Keras you either have the data augmentation with ImageDataGenerator or lazy loading of images with "flow_from_directory" and there is no flow_from_csv

Model finetuning with custom PyCaffe weights

Train_test_split, PyTorch version and Keras version

Weighted sampling training so that the model view rare cases more often

Custom Sampler creation, example for the balanced sampler

Saving snapshots each epoch

Loading the best snapshot for prediction

Failed word embeddings experiments to combine image and text data

Combined weighted loss function (softmax for unique weather tags, BCE for multilabel tags)

Selecting the best F2-threshold via stochastic search at the end of each epoch to maximize validation score. This is then saved along model parameter.

CNN-RNN combination (work in progress)

Amazon Forest Computer Vision