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