GabNetStats
GabNetStats is a free and open source software for visualizing neural network training statistics and metrics. It allows you to monitor loss, accuracy, and other metrics in real-time using interactive plots and charts.
neural-networks
training-statistics
metrics-monitoring
GabNetStats: Visualize Neural Network Training Statistics
Free and open source software for real-time visualization of neural network training metrics, including loss and accuracy monitoring with interactive plots and charts
What is GabNetStats?
GabNetStats is an open source neural network training visualizer and monitoring tool. It allows you to visualize loss, accuracy, and other metrics in real-time during training using interactive plots and charts.
Some key features of GabNetStats:
- Real-time metrics monitoring - See up-to-date metrics like loss, accuracy, IOU, mAP etc. while your model trains
- Interactive plots - Zoom, pan and analyze plot data with ease
- Support for TensorFlow, Keras, PyTorch and other frameworks
- Monitoring for local and remote sessions - View metrics for models training on your GPU or in the cloud
- Customizable metrics and logging - Visualize model-specific metrics beyond out-of-the-box options
- Lightweight and framework-agnostic - Integrate seamlessly with your existing pipeline
GabNetStats makes it easy to monitor neural network training runs, compare performance across models, and identify areas needing improvement. Its interactive plots help provide insights into training dynamics beyond static graphs. Developed with flexibility in mind, GabNetStats works with most models and frameworks.
GabNetStats Features
Features
- Real-time monitoring of neural network training statistics
- Interactive plots and charts for loss, accuracy, and other metrics
- Visualization for training and validation loss/accuracy
- Support for TensorFlow, Keras, PyTorch, and other frameworks
- Customizable metrics and plotting
- Logging and exporting data to CSV files
- Lightweight and easy to integrate
Pricing
- Open Source
Pros
Free and open source
Great for debugging and optimizing neural network training
Intuitive visualization and UI
Cross-framework support
Customizable and extensible
Cons
Limited to monitoring training statistics
Not suitable for large-scale distributed training
Only basic plotting functionality
Requires coding to integrate with models
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