Closed gianfrancodemarco closed 1 year ago
Can you try the same with the GIOU loss in Keras-CV https://github.com/keras-team/keras-cv ?
@bhack I've tried the IoULoss since the GIoULoss is not released yet, howewer the loss is stuck at 1.00 and the network is not learning, while improves using a classic mse loss. I'm overfitting on just on batch of images, using:
IoULoss(bounding_box_format="xyxy", mode="linear")
Try with pip install git+https://github.com/keras-team/keras-cv.git
@bhack in this way I was able to use it, but the loss is stuck at 1.8316 and no improving
I suggest to open a ticket on keras-cv as with components already re-implemented in keras-* we suggest to migrate on that library.
TensorFlow Addons is transitioning to a minimal maintenance and release mode. New features will not be added to this repository. For more information, please see our public messaging on this decision: TensorFlow Addons Wind Down
Please consider sending feature requests / contributions to other repositories in the TF community with a similar charters to TFA: Keras Keras-CV Keras-NLP
System information
Describe the bug I've looked at the paper introducing GIoU and my understanding is that admitted values for the loss range from -1 to 1. Trying to use it as a loss to train an object recognition model, I get GIoU values well over 1, and I don't know how to interpret them. This happens even in the tensorflow addons example:
How should this results be interpreted?