12- Object Detection with Tensorflow 2 - Re-Training, Iterative training, evaluation
🚀 Iterative Training: Learn the art of retraining your object detection model in iterations of 1000 steps. Discover the benefits of this approach in gradually improving model performance.
Download the Colab Notebooks: https://ayoubb.com/machine-learning/o...
📈 Understanding Steps vs. Epochs: Dive deep into the fundamental difference between training steps and epochs. Gain insights into how these metrics measure progress and performance during the training process.
🔄 Addressing Overfitting and Underfitting: Explore how iterative training provides a manual control mechanism, especially in scenarios where an auto-stop option might not be available. Learn to prevent overfitting and underfitting by carefully monitoring model performance.
📊 Performance Monitoring: Witness the real-time monitoring of train and validation metrics. Understand how to analyze loss curves, identifying patterns that guide you in making informed decisions to optimize your model.
🧠 Gradual Model Improvement: Grasp the concept of gradual model improvement through iterative training. See how this approach allows you to fine-tune your model over time, ensuring steady progress and enhanced performance.
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