Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).
MIT License
1.73k
stars
787
forks
source link
Using Class weights while training on Imbalanced training data #405
I have imbalanced annotations for my dataset. I want to introduce class weights in the fit_generator for every batch that I get after the train_generator is defined in train.py file. How do I get the information on classes/labels in every batch and the count which I can give class_weights to the fit_generator ?
I have imbalanced annotations for my dataset. I want to introduce class weights in the fit_generator for every batch that I get after the train_generator is defined in train.py file. How do I get the information on classes/labels in every batch and the count which I can give class_weights to the fit_generator ?