BhumikaMak commited on
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ux updates

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  1. app.py +3 -2
app.py CHANGED
@@ -8,7 +8,7 @@ yolov5_dff = os.path.join(os.getcwd(), "data/xai/yolov5_dff.png")
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  yolov8_dff = os.path.join(os.getcwd(), "data/xai/yolov8_dff.png")
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  architecture_description_yolov5 = """
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- # YOLOv5 Architecture
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  - **Backbone**: Uses **CSPDarknet53** for feature extraction with **ResNet**-like residual connections.
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  - **Neck**: **PANet** and **FPN** aggregate features at multiple scales.
@@ -23,7 +23,7 @@ architecture_description_yolov5 = """
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  """
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  architecture_description_yolov8s = """
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- # YOLOv8s Architecture
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  - **Backbone**: Uses **CSPDarknet** with efficient feature extraction layers.
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  - **Neck**: Incorporates **FPN** and **PANet** for multi-scale feature aggregation.
@@ -35,6 +35,7 @@ architecture_description_yolov8s = """
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  - **Multi-Task**: Supports tasks like classification, detection, and segmentation.
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  - **High Accuracy**: Achieves state-of-the-art results on small and medium datasets.
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  - **Lightweight**: Efficient architecture for smaller models with reduced computational cost.
 
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  """
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  yolov8_dff = os.path.join(os.getcwd(), "data/xai/yolov8_dff.png")
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  architecture_description_yolov5 = """
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+ ### YOLOv5 Architecture
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  - **Backbone**: Uses **CSPDarknet53** for feature extraction with **ResNet**-like residual connections.
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  - **Neck**: **PANet** and **FPN** aggregate features at multiple scales.
 
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  """
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  architecture_description_yolov8s = """
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+ ### YOLOv8s Architecture
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  - **Backbone**: Uses **CSPDarknet** with efficient feature extraction layers.
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  - **Neck**: Incorporates **FPN** and **PANet** for multi-scale feature aggregation.
 
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  - **Multi-Task**: Supports tasks like classification, detection, and segmentation.
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  - **High Accuracy**: Achieves state-of-the-art results on small and medium datasets.
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  - **Lightweight**: Efficient architecture for smaller models with reduced computational cost.
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+
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  """
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