endernewton/c2board

Tensorboard for Caffe2

38
/ 100
Emerging

This tool helps machine learning engineers working with Caffe2 models easily visualize their neural network architectures and track training progress. It takes Caffe2 computation graph definitions and training metrics (like loss, accuracy, and image outputs) as input. It then converts this information into a format compatible with TensorBoard, allowing engineers to use TensorBoard's powerful interface to understand model behavior and performance.

No commits in the last 6 months.

Use this if you are developing or training deep learning models using Caffe2 and need a visual way to inspect your model's structure and monitor training statistics over time.

Not ideal if you are not using Caffe2 for your deep learning models, as it's specifically designed for that framework.

deep-learning neural-networks model-training machine-learning-engineering model-visualization
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 15 / 25

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Stars

33

Forks

6

Language

Python

License

MIT

Last pushed

Sep 29, 2018

Commits (30d)

0

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