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README.md
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@@ -95,8 +95,7 @@ The model demonstrated significant improvement during training, as shown in the
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| mAP50 | 0.089 | 0.598 |
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| mAP50-95 | 0.057 | 0.499 |
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Initially, the model would enthusiastically label almost anything as a duck, while only finding a few actual ducks
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### Model Statistics
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- Layers: 168
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- Parameters: 3,005,843
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| mAP50 | 0.089 | 0.598 |
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| mAP50-95 | 0.057 | 0.499 |
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Initially, the model would enthusiastically label almost anything as a duck (teddy bear), while only finding a few actual ducks - infrequently being correct when it claimed to have found a duck. The improved model is much more discerning: now, when it says it's found a duck, it's more likely to have actually identified a duck. While this training approach reduced overall sensitivity to duck detection, testing in our specific deployment environment showed improved recall under specific circumstances, suggesting better alignment with real-world conditions. This increased reliability, combined with better accuracy in placing bounding boxes around actual ducks, makes the final model much more practical for real-world use. When used in a controlled environmnet the incrase in accuraccy may be offset by the decrease in recall, though environment-specific data would certainly be helpful. Adjustments to hyperparameters provided a wide range of outcomes suggesting significant potential for additional improvement.
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### Model Statistics
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- Layers: 168
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- Parameters: 3,005,843
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