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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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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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if model and model.split(".")[-1] not in ("pt", "yaml", "yml"):
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raise NotImplementedError("RT-DETR only supports creating from *.pt, *.yaml, or *.yml files.")
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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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