CalculatedContent/WeightWatcher

Health v2 · maintenance only

Adoption not part of the score

1772 stars · 145 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

WeightWatcher is an open-source Python diagnostic tool for analyzing pre-trained deep neural networks without access to training or test data. It applies Random Matrix Theory and heavy-tailed self-regularization theory to predict test accuracy, detect over/under-training, and flag issues in model compression or fine-tuning.

Use cases

• predict test accuracy of a neural network without test data
• check if my model is over-trained or over-parameterized
• analyze pretrained pytorch or keras model layer quality
• detect problems when fine-tuning or compressing a model
• compare quality of different deep learning models
• get layer-level warning labels for under-trained layers

When to choose

• you need data-free diagnostics of trained Conv2D or Dense layer models in PyTorch or Keras
• you want to rank or compare models by predicted accuracy without running evaluations
• you are researching why deep learning works or model quality metrics

When to avoid

• you need full model evaluation with real test data
• you work with architectures beyond Conv2D and Dense layers, like transformers or attention layers
• you need production model monitoring rather than offline analysis

Facets

library · maturity active

machine-learning monitoring analytics benchmarking deep-learning machine-learning developer-tools data-science python neural-network-analysis random-matrix-theory model-diagnostics heavy-tailed-self-regularization model-quality pytorch keras

Member repositories

For agents

markdown · JSON · MCP: product_card(name="CalculatedContent/WeightWatcher")