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MKgoud
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License-Plate-Recognizer

Object Detection
License-Plate-Recognizer
Yolov8m
Object detection
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@@ -3,8 +3,8 @@ license: mit
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  pipeline_tag: object-detection
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  tags:
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  - License-Plate-Recognizer
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- - YOLOv8
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- - DeepLearning
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  ---
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  **License Plate Detection Model using YOLOv8**
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  =============================================
@@ -32,14 +32,15 @@ The model was trained using the YOLOv8 architecture with the following hyperpara
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  **Model Performance**
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  ---------------------
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-
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  The model achieves the following performance metrics on the validation set:
 
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  * mAP (mean Average Precision): 0.92
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  * AP (Average Precision) for license plates: 0.95
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  * Recall: 0.93
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  * Precision: 0.94
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-
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  **Usage**
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  -----
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  pipeline_tag: object-detection
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  tags:
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  - License-Plate-Recognizer
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+ - Yolov8m
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+ - Object detection
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  ---
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  **License Plate Detection Model using YOLOv8**
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  =============================================
 
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  **Model Performance**
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  ---------------------
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+ ![confusion_matrix.png](https://cdn-uploads.huggingface.co/production/uploads/6537b44c01281b544234189c/6Wr5WE6dPC_6AisU07hEy.png)
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  The model achieves the following performance metrics on the validation set:
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+ ![val_batch1_pred.jpg](https://cdn-uploads.huggingface.co/production/uploads/6537b44c01281b544234189c/V37GbUwKr-CXaNunUOdqc.jpeg)
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  * mAP (mean Average Precision): 0.92
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  * AP (Average Precision) for license plates: 0.95
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  * Recall: 0.93
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  * Precision: 0.94
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+ ![results.png](https://cdn-uploads.huggingface.co/production/uploads/6537b44c01281b544234189c/_dDT5Bp5l4nGoTf8k9kMs.png)
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  **Usage**
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  -----
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