Task from Deep Learning – Summer 2019/20. Part of this code was provided by Milan Straka.
The goal of this assignment is to use pretrained EfficientNet-B0 model to achieve best accuracy in CAGS classification.
The CAGS dataset consists of images of cats and dogs of size 224×224, each classified in one of the 34 breeds
and each containing a mask indicating the presence of the animal. To load the dataset, use the cags_dataset.py module.
The dataset is stored in a TFRecord
file and each element is encoded as a tf.train.Example. Therefore the dataset is
loaded using tf.data API and each entry can be decoded using .map(CAGS.parse) call.
To load the EfficientNet-B0, we use the the provided efficient_net.py module.
The model is build, trained and tested in cags_classification.py.
An example performing classification of given images is available in image_classification.py.
The goal of this project is to use pretrained EfficientNet-B0 model to achieve best image segmentation IoU score
on the CAGS dataset.
A mask is evaluated using intersection over union (IoU) metric, which is the intersection of the gold and predicted
mask divided by their union, and the whole test set score is the average of its masks' IoU. A TensorFlow compatible
metric is implemented by the class CAGSMaskIoU of the cags_segmentation_eval.py module, which can further be used to
evaluate a file with predicted masks.
The model is build, trained and tested in cags_segmentation.py.