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MMCV

DVCLive allows you to easily add experiment tracking capabilities to your OpenMMlab projects.

About MMCV

MMCV is a foundational library for computer vision research and supports many research projects part of OpenMMLab.

Usage

To start using the DVCLive you just need to add the following line to your config file of any OpenMMlab project:

log_config = dict(
    interval=100,
    hooks=[
-        dict(type='TextLoggerHook')
+        dict(type='TextLoggerHook'),
+        dict(type='DvcliveLoggerHook')
    ]
)

This will use the registered DvcliveLoggerHook to generate metrics logs and summaries during training.

💡Without requiring additional modifications to your training code, you can use DVCLive alongside DVC. See DVCLive with DVC for more info.

Parameters

Parameters

  • model_file - (None by default) - The name of the file where the model will be saved at the end of each step.

  • **kwargs - Any additional arguments will be passed to Live.

Examples

  • Using model_file.
log_config = dict(
    interval=100,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='DvcliveLoggerHook', model_file="my_model.pth")
    ]
)
  • Using **kwargs to customize Live.
log_config = dict(
    interval=100,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(
            type='DvcliveLoggerHook',
            path="custom_path",
            summary=False)
    ]
)
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