Metalhead.jl provides standard machine learning vision models for use with Flux.jl. The architectures in this package make use of pure Flux layers, and they represent the best-practices for creating modules like residual blocks, inception blocks, etc. in Flux. Metalhead also provides some building blocks for more complex models in the Layers module.
]add Metalhead| Model Name | Function | Pre-trained? |
|---|---|---|
| VGG | VGG |
Y (w/o BN) |
| ResNet | ResNet |
Y |
| WideResNet | WideResNet |
Y |
| GoogLeNet | GoogLeNet |
N |
| Inception-v3 | Inceptionv3 |
N |
| Inception-v4 | Inceptionv4 |
N |
| InceptionResNet-v2 | Inceptionv3 |
N |
| SqueezeNet | SqueezeNet |
Y |
| DenseNet | DenseNet |
N |
| ResNeXt | ResNeXt |
Y |
| MobileNetv1 | MobileNetv1 |
N |
| MobileNetv2 | MobileNetv2 |
N |
| MobileNetv3 | MobileNetv3 |
N |
| EfficientNet | EfficientNet |
N |
| MLPMixer | MLPMixer |
N |
| ResMLP | ResMLP |
N |
| gMLP | gMLP |
N |
| ViT | ViT |
N |
| ConvNeXt | ConvNeXt |
N |
| ConvMixer | ConvMixer |
N |
To contribute new models, see our contributing docs.
You can find the Metalhead.jl getting started guide here.