EdgeNeXt

Category: Efficient/Mobile · Code: timm/models/edgenext.py · Architectures: 5 · Pretrained weights: 6 (6 with ImageNet-1k results)

EdgeNeXt is a hybrid CNN-Transformer for mobile devices that combines depthwise convolution encoders with split depth-wise transpose attention (SDTA) encoders, which apply attention across channels.

Papers

• EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
• Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results

Usage

See the Quickstart for loading pretrained weights, image preprocessing and fine-tuning, and Feature Extraction for using the models as backbones.

>>> import timm
>>> model = timm.create_model('edgenext_base.in21k_ft_in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)

Model API support

✓ supported, ✓ (square) works for square sizes only, ✗ not supported, – not needed (the model already accepts any input size). Checked by running each feature on a randomly initialized model (EdgeNeXt with edgenext_xx_small at 256), other architectures of the same class are expected to behave the same. See the Model API page for details.

Architectures

GMACs and parameter counts are for the default input size and classifier of each architecture, from the benchmark results.

Pretrained weights

Top-1 is ImageNet-1k validation accuracy from results-imagenet.csv at the listed eval size, - if the weight has no ImageNet-1k result (e.g. pretraining or image-text weights). Classes is the size of the pretrained classifier, none for weights without one.