RepGhost

Category: Efficient/Mobile · Code: timm/models/repghost.py · Architectures: 8 · Pretrained weights: 8 (8 with ImageNet-1k results)

RepGhostNet replaces the feature concatenation of the Ghost module with re-parameterized feature reuse, folded into a single convolution path at inference.

Papers

RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization

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('repghostnet_200.in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)

Model API support

Feature	`RepGhostNet`
`features_only=True` feature maps	✓ (strides 2, 4, 8, 16, 32)
`forward_intermediates()`	✓
`prune_intermediate_layers()`	✓
Any input size, no changes needed	✓
`set_input_size()`	–
`dynamic_img_size=True`	–
`set_grad_checkpointing()`	✓
Layer-wise LR decay (`group_matcher()`)	✓
`reset_classifier()`	✓

✓ 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 (RepGhostNet with repghostnet_050 at 224), other architectures of the same class are expected to behave the same. See the Model API page for details.

Architectures

Architecture	Params (M)	GMACs	Input size	Pretrained weights
`repghostnet_050`	2.31	0.05	224	1
`repghostnet_058`	2.54	0.06	224	1
`repghostnet_080`	3.27	0.10	224	1
`repghostnet_100`	4.06	0.15	224	1
`repghostnet_111`	4.52	0.18	224	1
`repghostnet_130`	5.46	0.24	224	1
`repghostnet_150`	6.55	0.31	224	1
`repghostnet_200`	9.77	0.53	224	1

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.

Weight	Input size	Top-1 (%)	Eval size	Classes	License	Hub
`repghostnet_050.in1k`	224	66.978	224	1000	mit	timm
`repghostnet_058.in1k`	224	68.938	224	1000	mit	timm
`repghostnet_080.in1k`	224	72.240	224	1000	mit	timm
`repghostnet_100.in1k`	224	74.208	224	1000	mit	timm
`repghostnet_111.in1k`	224	75.060	224	1000	mit	timm
`repghostnet_130.in1k`	224	76.378	224	1000	mit	timm
`repghostnet_150.in1k`	224	77.474	224	1000	mit	timm
`repghostnet_200.in1k`	224	78.802	224	1000	mit	timm

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