Neptune - Detectron2 integration

Experiment tracking for Detectron2-trained models.

What will you get with this integration?

• Log, organize, visualize, and compare ML experiments in a single place
• Monitor model training live
• Version and query production-ready models and associated metadata (e.g., datasets)
• Collaborate with the team and across the organization

What will be logged to Neptune?

• Model configuration,
• Training code and Git information,
• System metrics and hardware consumption,
• Other metadata

Resources

• Documentation
• Code example on GitHub
• Example project in the Neptune app

Example

In the following example, we set the Trainer to save model checkpoints every 10th epoch. Neptune will upload those checkpoints and metrics at the same interval.

neptune_run = neptune.init_run(
    project="workspace-name/project-name",
    name="My detectron2 run",
    tags = ["validation"],
    capture_stdout=False,
)

neptune_hook = NeptuneHook(
    run=neptune_run,
    log_checkpoints=True,
    metrics_update_freq=10,
)

Support

If you got stuck or simply want to talk to us, here are your options:

• Check our FAQ page.
• You can submit bug reports, feature requests, or contributions directly to the repository.
• Chat! In the Neptune app, click the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP).
• You can just shoot us an email at support@neptune.ai.