We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Change from: compute_loss = torch_losses.Loss(device=device, nc=num_classes)
compute_loss = torch_losses.Loss(device=device, nc=num_classes)
to compute_loss = torch_losses.Loss(device=device, nc=num_classes, input_shape=imgsz)
compute_loss = torch_losses.Loss(device=device, nc=num_classes, input_shape=imgsz)
keras_cv_attention_models/keras_cv_attention_models/yolov8/train.py
Line 79 in 9ee3d73
(I was using EfficientNet backbone, resolution of inputs 320x320, and keras backend - shapes mismatched in loss function)
The text was updated successfully, but these errors were encountered:
fix yolov8 coco train
6389ec1
Ya, you are right. Fix is updated. It's actually updated in torch_coco_train_script.py, but missed here.
Sorry, something went wrong.
No branches or pull requests
Change from:
compute_loss = torch_losses.Loss(device=device, nc=num_classes)
to
compute_loss = torch_losses.Loss(device=device, nc=num_classes, input_shape=imgsz)
keras_cv_attention_models/keras_cv_attention_models/yolov8/train.py
Line 79 in 9ee3d73
(I was using EfficientNet backbone, resolution of inputs 320x320, and keras backend - shapes mismatched in loss function)
The text was updated successfully, but these errors were encountered: