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# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Interface for Baidu's RT-DETR, a Vision Transformer-based real-time object detector. RT-DETR offers real-time
performance and high accuracy, excelling in accelerated backends like CUDA with TensorRT. It features an efficient
hybrid encoder and IoU-aware query selection for enhanced detection accuracy.
For more information on RT-DETR, visit: https://arxiv.org/pdf/2304.08069.pdf
"""
from ultralytics.engine.model import Model
from ultralytics.nn.tasks import RTDETRDetectionModel
from .predict import RTDETRPredictor
from .train import RTDETRTrainer
from .val import RTDETRValidator
class RTDETR(Model):
"""
Interface for Baidu's RT-DETR model. This Vision Transformer-based object detector provides real-time performance
with high accuracy. It supports efficient hybrid encoding, IoU-aware query selection, and adaptable inference speed.
Attributes:
model (str): Path to the pre-trained model. Defaults to 'rtdetr-l.pt'.
"""
def __init__(self, model="rtdetr-l.pt") -> None:
"""
Initializes the RT-DETR model with the given pre-trained model file. Supports .pt and .yaml formats.
Args:
model (str): Path to the pre-trained model. Defaults to 'rtdetr-l.pt'.
Raises:
NotImplementedError: If the model file extension is not 'pt', 'yaml', or 'yml'.
"""
super().__init__(model=model, task="detect")
@property
def task_map(self) -> dict:
"""
Returns a task map for RT-DETR, associating tasks with corresponding Ultralytics classes.
Returns:
dict: A dictionary mapping task names to Ultralytics task classes for the RT-DETR model.
"""
return {
"detect": {
"predictor": RTDETRPredictor,
"validator": RTDETRValidator,
"trainer": RTDETRTrainer,
"model": RTDETRDetectionModel,
}
}
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