Can I tune the rt-detr model?

aluissp commented 2 months ago

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Question

Currently, I'm training the RT-DETR model on my own custom dataset. According to the documentation, there's a model.tune() method to optimize the hyperparameters. However, when I inspected the source code, this method runs a YOLO model instead of RT-DETR. I'm a little bit confused about it. Here's the code:

from ultralytics import RTDETR

# Load a COCO-pretrained RT-DETR-l model
model = RTDETR('rtdetr-l.pt')

model.tune(data='data.yaml', device=[0, 1], batch=8, epochs=40, iterations=35, imgsz=640)

Tune method:


def tune():
    ...
    return Tuner(args=args, _callbacks=self.callbacks)(model=self, iterations=iterations)

class Tuner:
    ...
    def __call__(self, model=None, iterations=10, cleanup=True):
                ...
                cmd = ["yolo", "train", *(f"{k}={v}" for k, v in train_args.items())]
                return_code = subprocess.run(cmd, check=True).returncode
                ckpt_file = weights_dir / ("best.pt" if (weights_dir / "best.pt").exists() else "last.pt")
                metrics = torch.load(ckpt_file)["train_metrics"]
                assert return_code == 0, "training failed"

Additional

No response

github-actions[bot] commented 2 months ago

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Y-T-G commented 2 months ago

You can't.

glenn-jocher commented 2 months ago

@Y-T-G thank you for your question! Currently, the model.tune() method is designed for YOLO models and not specifically for RT-DETR models. As a result, it may not work as expected for RT-DETR.

For tuning RT-DETR models, you can manually adjust hyperparameters in the training script. Here's an example of how you can set custom hyperparameters:

from ultralytics import RTDETR

# Load a COCO-pretrained RT-DETR-l model
model = RTDETR('rtdetr-l.pt')

# Train the model with custom hyperparameters
results = model.train(
    data='data.yaml',
    epochs=40,
    batch=8,
    imgsz=640,
    device=[0, 1],
    lr0=0.01,  # initial learning rate
    momentum=0.937,  # momentum
    weight_decay=0.0005,  # weight decay
    ...
)

You can find more details on available hyperparameters in the Ultralytics documentation.

If you have any further questions or need additional assistance, feel free to ask! 😊

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