How to further improve validation accuracy in multiclass semantic segmentation

Hi.

I am working on this tut. I have improved validation accuracy from 76% to 86% by simply training on the entire dataset: https://keras.io/examples/vision/deeplabv3_plus/

This is a DeepLabv3+ model with ResNet50 backend. I tried to improve accuracy further by augmenting the data during training with this function:

def image_augmentation(img): img = tf.image.random_flip_left_right(img) img = tfa.image.rotate(img, rand_degree()) 

However, using any augmentation just decreased validation accuracy. For some reason, the model performs better without any augmentation.

I also tried to change the …

computervision segmentation semantic validation