snap-research/EfficientFormer

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1116 stars · 95 forks observed · 2026-08-28

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

A PyTorch implementation of EfficientFormer and EfficientFormerV2, efficient vision transformer model families designed to run at MobileNet-level speed and size on mobile devices. It includes pretrained ImageNet-1K checkpoints, downstream detection/segmentation variants, and profiling tools.

Use cases

• run vision transformers as fast as mobilenet on iphone
• download pretrained efficientformer checkpoints for imagenet classification
• use efficient vision transformer backbone for object detection
• use efficientformer for semantic segmentation
• benchmark transformer latency on mobile devices
• find efficient transformer architectures for resource-constrained hardware

When to choose

• you need low-latency image classification on mobile or edge devices
• you want a vision transformer backbone with MobileNet-level size and speed
• you need pretrained checkpoints for detection or segmentation with efficient backbones

When to avoid

• you need state-of-the-art accuracy on high-end GPUs regardless of latency
• you need a maintained library with frequent updates or broad ecosystem support
• you need non-PyTorch frameworks like TensorFlow or JAX

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision image-processing mobile-development python cross-platform vision-transformer efficient-inference imagenet pytorch model-zoo neural-architecture-search mobile-deployment semantic-segmentation object-detection mobile

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