![]() Evolve hyperparameters to improve performance.Export to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats.Validate accuracy on train, val and test splits.Run CLI or Python inference on new images and videos.Once your model is trained you can use your best checkpoint best.pt to: plots import plot_results plot_results( 'path/to/results.csv') # plot 'results.csv' as 'results.png' You can also plot any results.csv file manually:įrom utils. Results file results.csv is updated after each epoch, and then plotted as results.png (below) after training completes. This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. Training results are automatically logged with Tensorboard and CSV loggers to runs/train, with a new experiment directory created for each new training as runs/train/exp2, runs/train/exp3, etc. Explore the ClearML Tutorial for details! Local Logging This will help you keep track of your data without adding extra hassle. You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers). but ClearML also tracks uncommitted changes and installed packages for example. You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. ![]() ![]()
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