Features
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Graphs in ClearML - Display graphs for any Darknet compatible networks.
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For classifiers:
- Accuracy (acc);
- Average loss (avg_loss);
- learning rate (lr).
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For detectors:
- Mean average precision 50% (mAP@0.50);
- Precision;
- Recall;
- F1-score;
- Average loss (avg_loss);
- learning rate (lr).
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Logs in ClearML - Displays logs from Darknet in ClearML.
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Saving weights in ClearML - Ability to save weights in ClearML with flexible adjustment of parameters.
Available options:
- Darknet.is_save_weights - Ability to disable saving weights (for experiments).
- Darknet.save_weights_from_n_iterations - Ability to save weights from the start of N iterations. For example, N=10000.
- Darknet.save_weights_every_n_iterations - Ability to save weights every N iterations. For example, N=5000.
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Additional function of data splitting - Allows you to divide the data set into selections.
📚 Click to see example
from clearml import Dataset from clearml_darknet.utils import split_dataset dataset_path = Dataset.get(dataset_name='dataset-example', dataset_project='Tests/darknet').get_local_copy() train, valid = split_dataset( dataset_path=dataset_path, ratio=0.7, shuffle=True )
Requirements
Installation
Before running, be sure to clone the Darknet repository and compile for the appropriate operating system.
$ pip install clearml-darknet-py
Examples
- An example of a detector training script - train_detector.py
- An example of a classificator training script - train_classifier.py
- Detector optimization script example - optimize_detector.py
For examples and use cases, check the examples folder.
License
Clearml Darknet is MIT licensed as listed in the LICENSE file.