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README.md
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@@ -42,8 +42,15 @@ Annotations included manually sorting each of the 60 breeds into a catagory base
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Less than a .04 difference between classes for each metric.
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### *Examples Of Classes*
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### *Visualizations*
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### *Performance Analysis*
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***
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# Limitations and Biases
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### *Known failure cases*
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Less than a .04 difference between classes for each metric.
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### *Examples Of Classes*
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### *Visualizations*
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<img alt="Confusion Matrix" src="https://huggingface.co/cvtechniques/DogTypeDetection/resolve/main/confusion_matrix_normalized.png" width="700"></img>
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<img alt="F1-Confidence Graph" src="https://huggingface.co/cvtechniques/DogTypeDetection/resolve/main/BoxF1_curve.png" width="700"></img>
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<img alt="Precsiosn-Confidence Graph" src="https://huggingface.co/cvtechniques/DogTypeDetection/resolve/main/BoxP_curve.png"></img>
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### *Performance Analysis*
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This model had high metrics across each of the classes, meeting the success threshold in precision, recall and F1 score. The confusion matrics shows some slight over guessing, as each of the classes had a 25% to 40% rates of being prediceted when that area was actual background. The model also predicted small dogs as large dogs 10% of the time, which was right at the limit set before training. That being said, the matrix still has high values of 80%-85% along the true postive diagonal. The 100% precioson peak at
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***
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# Limitations and Biases
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### *Known failure cases*
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