Advanced Customization

Sorry, I missed it above. I saw it))) Delete this comment.

@ablyzniuk to set a custom loss function, you can override the init_criterion method in your custom model class, as shown in the Advanced Customization documentation. This method allows you to define and initialize your loss function according to your requirements. Let me know if you need an example!

@glen-jocher thank you) I will be glad and thankfull if you will give a detailed example of training default Ultralytics Yolo(5-11 versions) with custom trainer e.t.c. I think there must be much easier and convinient way opposite what I did (I edited source code and overrides DetectionTrainer methods after)

For custom training without modifying source code, subclass DetectionTrainer and override methods like this:

from ultralytics.models.yolo.detect import DetectionTrainer
from ultralytics.nn.tasks import DetectionModel

class CustomModel(DetectionModel):
    def init_criterion(self):
        # Your custom loss initialization here
        pass

class CustomTrainer(DetectionTrainer):
    def get_model(self, cfg=None, weights=None, verbose=True):
        return CustomModel(cfg, nc=self.data["nc"], verbose=verbose)  # Replace with your model config

trainer = CustomTrainer(overrides={'model': 'yolo26n.yaml', 'data': 'coco128.yaml', 'epochs': 100})
trainer.train()

This maintains upgrade compatibility while allowing customization. The Advanced Customization Guide shows additional methods you can override.

Yes, it's definitely possible to define custom DataLoader and Augmentation pipelines in DetectionTrainer! 🚀 To do so, you'd primarily override the get_dataloader() method in a subclass of DetectionTrainer. Here's a brief example on how you might go about customizing your DataLoader:

from ultralytics.models.yolo.detect import DetectionTrainer

class CustomTrainer(DetectionTrainer):
    def get_dataloader(self, ...):
        # Your custom DataLoader logic here
        ...

For augmentations, you can integrate them within your custom DataLoader or modify datasets accordingly. If you need further customization or to include a custom Augmentation pipeline directly tied with the models' training loop, you could adjust or extend methods in the model itself or within the get_dataloader method to apply augmentations.

Don't forget to explore our Advanced Customization section and BaseTrainer Reference for more details on customizing training routines, models, and DataLoaders. 😄 Happy coding!

Hello! It seems like there’s been a little confusion regarding the overrides={...} usage, and I appreciate you reaching out for clarification. 🙌

To directly answer your question, the overrides={...} parameter is designed to accept a dictionary of configuration overrides, not a file. It's intended to programmatically override specific configuration entries. For example:

from ultralytics.models.yolo.detect import DetectionTrainer

# Example override dictionary
overrides = {
    "batch": 16,
    "imgsz": 640,
}

trainer = DetectionTrainer(overrides=overrides)

If you're looking to use the configurations from an args.yaml file created after training, you'd first need to read this YAML file into a Python dictionary, which you could then pass to overrides. Here's a quick way to do it:

import yaml

# Load your args.yaml content into a dictionary
with open('path/to/your/args.yaml', 'r') as file:
    yaml_content = yaml.safe_load(file)

# Now, pass the loaded dictionary to overrides
trainer = DetectionTrainer(overrides=yaml_content)

Regarding the error you're encountering, it appears there's a misunderstanding in how the overrides dictionary is being used or possibly an issue with the implementation. If following the above pattern doesn't resolve your issue, could you please share more details about what you're trying to achieve with the overrides? This way, I or someone from the community can offer more targeted advice. 😊

Thank you for your patience, and looking forward to helping you get this sorted!