mmaaz60/EdgeNeXt

[CADL'22, ECCVW] Official repository of paper titled "EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications".

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Introduces split depth-wise transpose attention (SDTA) that groups channels and combines depth-wise convolution with self-attention to encode multi-scale features efficiently. Achieves 79.4% ImageNet-1K accuracy with 5.6M parameters across classification, detection, and segmentation tasks, with PyTorch implementation supporting distributed training on multi-GPU setups and model variants ranging from 1.3M to 18.5M parameters.

411 stars. No commits in the last 6 months.

Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 16 / 25

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Stars

411

Forks

45

Language

Python

License

MIT

Last pushed

Jul 25, 2023

Commits (30d)

0

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