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  We introduce a challenging dataset for identifying machine parts from real photos,
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  featuring images of 102 parts from a labeling machine. This dataset was developed
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  with the complexity of real-world scenarios in mind and highlights the complexity
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  orientations. We also use material metadata and apply one of 21 texture materials
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  to the objects. We render all images at 512x512 pixels. The real photo set consists of
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  raw images captured under varying conditions using different cameras, including
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- varied lighting, backgrounds, and environmental factors.
 
 
 
 
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+ ## Dataset Description
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  We introduce a challenging dataset for identifying machine parts from real photos,
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  featuring images of 102 parts from a labeling machine. This dataset was developed
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  with the complexity of real-world scenarios in mind and highlights the complexity
 
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  orientations. We also use material metadata and apply one of 21 texture materials
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  to the objects. We render all images at 512x512 pixels. The real photo set consists of
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  raw images captured under varying conditions using different cameras, including
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+ varied lighting, backgrounds, and environmental factors.
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+
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+ ### Citation Information
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+