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Book Overview & Buying
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Table Of Contents
Hands-On Computer Vision with Detectron2
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Before fine-tuning models, it is essential to understand the evaluation metrics that are measured during training, such as training losses (for example, classification losses and localization losses), learning rate changes, and validation measurements on the test set (for example, [email protected]), which change over time. These metrics allow us to understand the training processes for debugging and fine-tuning models. Detectron2’s logger automatically logs the evaluation metrics for training and testing data in a format that is usable by TensorBoard.
TensorBoard (https://www.tensorflow.org/tensorboard) offers tools and visualizations so that we can explore training and evaluation processes in machine learning experiments. The following code snippet downloads the training logs for the two experiments we carried out in Chapter 5 and unzips them:
# download and extract
!wget {url_to_object_detector.zip}
!wget {url_to_object_detector_hook...