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@@ -6,6 +6,15 @@ pipeline_tag: object-detection
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  tags:
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  - printed-circuit-boards
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  library_name: ultralytics
 
 
 
 
 
 
 
 
 
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  metrics:
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  - f1 - 93.8%
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  - mAP50 - 93.0%
@@ -15,6 +24,8 @@ metrics:
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  There are [a lot of models](https://universe.roboflow.com/roboflow-100/printed-circuit-board/model/3) for detecting components within a Printed Circuit Board (PCB), but not as many for detecting which pixels (if any) in an image contain the PCB itself. Being able to determine if and where a PCB is in an image is useful for [calculating its size to estimate carbon footprint]((https://github.com/SanderGi/LCA)), as a preprocessing step for detecting components, to limit the amount of image more expensive PCB defect detection models have to process, and more.
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  ## Usage
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  1. Download [`the model weights`](https://huggingface.co/SanderGi/PCB-OBB/resolve/main/best.pt?download=true)
 
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  tags:
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  - printed-circuit-boards
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  library_name: ultralytics
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+ model-index:
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+ - name: ultralytics/yolo11
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+ results:
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+ - task:
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+ type: object-detection
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+ metrics:
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+ - type: f1
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+ value: 93.8%
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+ name: F1 Score
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  metrics:
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  - f1 - 93.8%
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  - mAP50 - 93.0%
 
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  There are [a lot of models](https://universe.roboflow.com/roboflow-100/printed-circuit-board/model/3) for detecting components within a Printed Circuit Board (PCB), but not as many for detecting which pixels (if any) in an image contain the PCB itself. Being able to determine if and where a PCB is in an image is useful for [calculating its size to estimate carbon footprint]((https://github.com/SanderGi/LCA)), as a preprocessing step for detecting components, to limit the amount of image more expensive PCB defect detection models have to process, and more.
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+ Read more [here](https://github.com/SanderGi/PCB-Detection).
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+
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  ## Usage
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  1. Download [`the model weights`](https://huggingface.co/SanderGi/PCB-OBB/resolve/main/best.pt?download=true)