endernewton/c2board
Tensorboard for Caffe2
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.
Stars
33
Forks
6
Language
Python
License
MIT
Category
Last pushed
Sep 29, 2018
Commits (30d)
0
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