Cross validation in Object detection

TensorFlow General Discussion
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Anyone knows how to do cross-validation in Object detection? I am using the model maker

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@Elven_Kim Welcome to Tensorflow Forum !
While Model Maker doesn’t offer built-in cross-validation for object detection, you can implement it using additional tools and techniques. Here are two approaches:

1. Manual Cross-Validation:

  • Split your dataset : Divide your labeled images into training, validation, and test sets. Aim for a 70/20/10 split as a starting point.
  • Train multiple models : Train separate models on different combinations of training and validation sets. Ensure each model sees a unique set of validation data.
  • Evaluate performance : Evaluate each model’s performance on its respective validation set using metrics like mAP (mean Average Precision) or AP (Average Precision) for different object classes.
  • Analyze results : Compare the performance of different models on the validation sets. This will give you an estimate of the model’sgeneralizability and potential overfitting.

2. K-Fold Cross-Validation:

  • Divide your dataset : Similar to manual cross-validation, split your data into K folds (e.g., K=5).
  • Train and validate : For each fold, use the remaining folds for training and the current fold for validation. This creates K different training-validation pairs.
  • Average performance : Calculate the average performance (e.g., mAP) across all K validation sets. This provides a more robust estimate of the model’sgeneralizability.

Tools for Cross-Validation:

  • TensorFlow : Use libraries like TensorFlow Datasets and TensorFlow Object Detection API for data preparation and model training.
  • Scikit-learn : Leverage tools like KFold and cross_val_score for k-fold cross-validation.
  • Model Garden : While not directly supporting cross-validation, you can still use its training and evaluation functionalities within your manual or k-fold approach.

Additional Tips:

  • Consider using early stopping during training to prevent overfitting.
  • Monitor validation metrics during training to identify potential issues.
  • Experiment with different K values to find the optimal balance between bias and variance in your cross-validation estimates.

Let us know if this helps!