> For the complete documentation index, see [llms.txt](https://sign-language-center.gitbook.io/tech/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sign-language-center.gitbook.io/tech/articles/training-and-validation-metrics-analysis-for-model-performance-optimization.md).

# Training and Validation Metrics Analysis for Model Performance Optimization

19th training results of Sign Language Center model

## Training Losses (Top Row)

1. `train/box_loss`:
   * Decreases steadily over epochs, indicating better bounding box predictions.
   * The smooth line shows the overall trend, suggesting consistent improvement.
2. `train/cls_loss`:
   * The classification loss reduces significantly over time, implying better predictions for class labels.
3. `train/dfl_loss`:
   * Similar behavior as the box and classification losses; the quality of the model's predictions improves progressively.
4. `metrics/precision(B)`:
   * Precision starts high and stabilizes close to 1.0. This suggests the model is minimizing false positives effectively.
5. `metrics/recall(B)`:
   * Recall improves sharply and reaches close to 1.0, indicating the model is capturing nearly all relevant instances.

## Validation Losses (Bottom Row)

6. `val/box_loss`:
   * Shows a steady decrease, similar to the training box loss. Consistency between train and validation suggests no overfitting here.
7. `val/cls_loss`:
   * Decreases significantly, indicating improved classification performance on the validation set.
8. `val/dfl_loss`:
   * Follows a similar pattern as `train/dfl_loss`, with consistent performance across training and validation.

## Validation Metrics (Bottom Row)

9. `metrics/mAP50(B)`:
   * Mean Average Precision (mAP) at IoU threshold 50% increases rapidly and stabilizes close to 1.0, indicating high-quality predictions for detecting objects.
10. `metrics/mAP50-95(B)`:
    * The mAP across multiple IoU thresholds improves gradually and stabilizes. Some fluctuations early in training are common but stabilize later.

## ➔ Overall

* The model shows consistent improvement across all metrics and losses, with no major signs of overfitting or instability.
* Precision and recall are high, and validation losses align well with training losses, indicating a well-generalized model.


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