mapillary/inplace_abn

Health v2 · maintenance only

Adoption not part of the score

1333 stars · 183 forks observed · 2026-08-28

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

A PyTorch extension library implementing In-Place Activated BatchNorm (InPlace-ABN), which redefines BN plus nonlinear activation as a single in-place operation to reduce training GPU memory usage by up to 50%. It ships the iABN layer plus training scripts reproducing ImageNet classification and Mapillary Vistas semantic segmentation results.

Use cases

• reduce GPU memory usage when training deep networks in PyTorch
• train larger ResNet or ResNeXt models on limited GPU memory
• fit bigger batch sizes on a single GPU during CNN training
• fuse batch normalization and activation into one memory-efficient layer
• reproduce memory-optimized ImageNet or semantic segmentation training results

When to choose

• you train deep CNNs in PyTorch on Linux with CUDA >= 10 and GPU memory is the bottleneck
• you want to increase model size or batch size without upgrading hardware
• you need an invertible BN+activation layer compatible with recomputation-based backpropagation

When to avoid

• you develop or deploy on non-Linux platforms or older CUDA versions
• you need to transfer weights from networks trained with standard BatchNorm, since the scaling parameterization is incompatible
• you use a framework other than PyTorch or your models are small enough that memory savings do not matter

Facets

library · maturity stable

machine-learning deep-learning gpu-computing deep-learning machine-learning computer-vision gpu-computing python pytorch batch-normalization memory-optimization cuda-extension semantic-segmentation imagenet training-efficiency neural-networks linux gpu

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