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app.py
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@@ -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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- **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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@@ -23,7 +23,7 @@ architecture_description_yolov5 = """
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"""
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architecture_description_yolov8s = """
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-
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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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@@ -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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