Ultralytics
usage/callbacks/ #17484
giscus[bot]
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Replies: 5 comments 12 replies
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Hi, in the documentation above, I see that |
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@D3RK0 to apply data augmentation during training, you can use the |
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@glenn-jocher Many thanks for your quick response! I am dealing with following problem though: ProblemWhen I do My codeContent of
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The |
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@glenn-jocher Many thanks for answering! Okay, I have been able to access the batches via Problem... assigning the batch back (see also the code below) to
CodeQuestionHow can I return the augmented batch to the |
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You cannot directly assign to |
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👋 Hello, thank you for your interest in Ultralytics 🚀! It seems like you're diving into Ultralytics callbacks for various stages like training, validation, exporting, and prediction. That's a great way to customize your workflows! For comprehensive details, I recommend checking out our callbacks documentation where you'll find everything you need to know about implementing and customizing these callbacks. If you experience any issues or have specific questions, please feel free to share more details or examples, and be assured that a member of the Ultralytics team will assist you shortly 😊. In the meantime, upgrade to the latest pip install -U ultralyticsAlso, consider joining our dynamic community across different platforms for more engaging discussions and support:
Stay updated with our CI status to ensure smooth operation across different environments:
Is there anything specific you'd like to know or understand better about callbacks? Just let us know, and help is on the way! |
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When I'm using single device add_callback works well but multi gpu's not working any solution for this? |
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@ruslanraupoff for multi-GPU training with callbacks, ensure your callback function is properly registered in all processes. When using DDP mode, callbacks need to be added before the training starts and should be compatible with distributed training environments. If you're experiencing issues, verify that your callback logic is thread-safe and doesn't rely on process-specific resources. For implementation details, refer to our callbacks documentation: https://docs.ultralytics.com/usage/callbacks |
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Hello, I'm trying to implement a custom For some reason my custom callback function isn't getting called for each training run of the Tuner. Am I missing a step here? I've taken a look at the source code but don't see anything obviously wrong... Thanks! -James |
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@jnance34 Hi! At the moment, callbacks are not supported for model.tone(...). You can find a complete list of all supported callbacks here: https://docs.ultralytics.com/usage/callbacks/#all-callbacks |
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Hi can i and how use callback to freeze initial layers for number of epochs and unfreeze after that, is there any builtin function for that? i used freeze builtin function but it freezes for all the epochs. |
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also, after certain epochs the change in model training is minimal can we use callback then to change lr, how and what parameter to change ? |
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@drbilal216 yes, you can use callbacks to freeze/unfreeze layers dynamically during training. Here's how to freeze initial layers for specific epochs then unfreeze them: from ultralytics import YOLO
def dynamic_freeze_callback(trainer):
freeze_epochs = 10 # Freeze for first 10 epochs
freeze_layers = 10 # Number of layers to freeze
if trainer.epoch < freeze_epochs:
# Freeze layers
for i, (name, param) in enumerate(trainer.model.named_parameters()):
if i < freeze_layers:
param.requires_grad = False
else:
# Unfreeze all layers
for param in trainer.model.parameters():
param.requires_grad = True
model = YOLO("yolo11n.pt")
model.add_callback("on_train_epoch_start", dynamic_freeze_callback)
model.train(data="coco8.yaml", epochs=20)For dynamic learning rate adjustment when training plateaus, you can modify the scheduler within a callback: def dynamic_lr_callback(trainer):
if trainer.epoch > 50 and trainer.epoch % 10 == 0: # Every 10 epochs after epoch 50
for param_group in trainer.optimizer.param_groups:
param_group['lr'] *= 0.9 # Reduce LR by 10%
model.add_callback("on_train_epoch_end", dynamic_lr_callback) |
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@glenn-jocher Thanks for the code! I'm working on a low-quality PPE dataset. With a custom YAML, I improved YOLOv12n accuracy by 3%, and got similar results on YOLOv11n with better FPS. The default Nano model lets me use pretrained=True, but custom architectures start from scratch. Still, I can reuse best.pt as pretrained weights on the same YAML later. I'm now exploring argument tuning and callbacks for further improvements. I also tried freezing/unfreezing layers mid-training but saw no gains. Lastly, I integrated Flash into YOLOv12, but I don’t see any sign that it’s being used during training (tested on Colab and A100 80GB-server). |
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@drbilal216 great progress on your PPE dataset improvements! Your approach of using custom YAML configurations and reusing best.pt weights is solid. Regarding Flash integration with YOLO12, Flash Attention should automatically activate when available, but you can verify it's working by checking GPU memory usage patterns during training - Flash typically shows lower memory consumption. You can also add a simple print statement in your training callback to monitor memory usage and confirm Flash is engaged. |
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usage/callbacks/
Explore Ultralytics callbacks for training, validation, exporting, and prediction. Learn how to use and customize them for your ML models.
https://docs.ultralytics.com/usage/callbacks/
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