ufoym/imbalanced-dataset-sampler
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones. observed · 2026-08-28
Health v2 · maintenance only
64/100
- Activity 91
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3019
- days_rel: n/a
- days_push: 58
- n_releases_24m: 0
Adoption not part of the score
2326 stars · 266 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A PyTorch dataset sampler (ImbalancedDatasetSampler, pip package torchsampler) that rebalances class distributions during training by oversampling rare classes and undersampling frequent ones. It estimates sampling weights automatically and plugs directly into DataLoader.
Use cases
- handle imbalanced datasets in pytorch
- oversample minority classes during training
- prevent model bias toward majority class
- balance class distribution in dataloader
- train classifier on rare disease images
When to choose
- you train PyTorch models on class-imbalanced data
- you want automatic sampling weights without building a new balanced dataset
When to avoid
- you use TensorFlow or non-PyTorch frameworks
- you need advanced resampling like SMOTE
Facets
library · maturity stable
machine-learning data-science machine-learning deep-learning python pytorch sampler imbalanced-data oversampling undersampling
1 source
- readme: https://github.com/ufoym/imbalanced-dataset-sampler · fetched 2026-08-28 · 05e6c6e05b7a
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ufoym/imbalanced-dataset-sampler | main | 64 |
For agents
markdown · JSON · MCP: product_card(name="ufoym/imbalanced-dataset-sampler")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem