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<folder>imgs</folder>
<filename>8956.jpg</filename>
<path>/home/cai/data/电动车检测/imgs/8956.jpg</path>
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<name>electric</name>
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</object>
<object>
<name>electric</name>
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</object>
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<truncated>0</truncated>
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<difficult>0</difficult>
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</object>
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</object>
<object>
<name>electric</name>
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</object>
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</object>
<object>
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</object>
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</object>
<object>
<name>electric</name>
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<ymax>225</ymax>
</bndbox>
</object>
<object>
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<object>
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<pose>Unspecified</pose>
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<xmax>418</xmax>
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<xmax>327</xmax>
<ymax>263</ymax>
</bndbox>
</object>
<object>
<name>electric</name>
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<difficult>0</difficult>
<bndbox>
<xmin>287</xmin>
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<object>
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<object>
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<object>
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<object>
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<object>
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YOLOv5是一个流行的目标检测模型,它在图像中识别和定位对象方面表现出色。 数据集规模:您的数据集包含20000多个标注框,这意味着您拥有大量的训练样本,这有助于提高模型的泛化能力。 标注格式:使用XML文件进行标注是一种常见的做法,因为它可以提供丰富的信息,并且易于与不同的数据处理工具和库进行集成。 训练过程:您已经使用YOLOv5进行了训练,这可能包括了数据预处理、模型配置、训练、验证和测试等步骤。 模型性能:您提到的"准确率非常高"可能指的是模型在测试集上的表现,这可能包括了诸如平均精度(mAP)、精确度、召回率等指标。 应用场景:电动车、单车和三轮车的检测在交通监控、城市管理、智能交通系统等领域有着广泛的应用。 进一步优化:尽管当前的准确率已经很高,但您可能还想探索模型的进一步优化,比如通过调整超参数、使用数据增强技术、引入更复杂的模型架构等。 模型部署:训练完成后,您可能需要考虑如何将模型部署到实际应用中,这可能涉及到模型的转换、压缩和加速等步骤。 持续监控与更新:在模型部署后,持续监控其性能并根据需要进行更新也是非常重要的,以确保模型能够适应新的数据和环境变
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yolo 电动车,单车,三轮车数据集,包含20000多个标注框(亲测训练成功并且准确率高) (2000个子文件)
8956.xml 10KB
3280.xml 9KB
3359.xml 8KB
2417.xml 7KB
3239.xml 7KB
3140.xml 7KB
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3246.xml 5KB
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2593.xml 3KB
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3931.xml 3KB
3600.xml 3KB
8293.xml 3KB
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