Loading and Evaluating a Saved TensorFlow Bird Species Model

Model evaluation is the process of measuring a trained model’s performance on unseen test data and identifying which classes it predicts incorrectly. In my previous post “TensorFlow: Transfer Learning (Fine-Tuning) in Image Classification”, I described building a convolutional neural network based on EfficientNetB0 (initially trained on the ImageNet dataset), which underwent feature extraction and fine-tuning steps using the 400 Bird Species Dataset at Kaggle. This was an instructive experiment because ImageNet has only around 59 bird-related classes, while the Kaggle dataset has 400 bird species. Despite this difference in the underlying data, the final model reached 98.5% accuracy on the test set. This post loads the saved model from my deep learning repository and evaluates its performance in detail to determine which birds are not well predicted.

Getting the Dataset, Helper Functions, and Saved Model

Verifying the Bird Species Dataset with walk_directory()

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Loading the Saved Model with tf.keras.models.load_model()

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Finding Misclassified Bird Species by Prediction Confidence

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Predicting a Bird Species from a Web-Downloaded Image

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Conclusion: In-Depth Evaluation of a Saved Image Classifier

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References

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