diff --git a/src/Metalhead.jl b/src/Metalhead.jl
index 7eb81282..1efa7b9f 100644
--- a/src/Metalhead.jl
+++ b/src/Metalhead.jl
@@ -69,6 +69,9 @@ include("mixers/gmlp.jl")
 # ViTs
 include("vit-based/vit.jl")
 
+## ShuffleNet
+include("convnets/shufflenet.jl")
+
 # Load pretrained weights
 include("pretrain.jl")
 
@@ -81,7 +84,7 @@ export AlexNet, VGG, ResNet, WideResNet, ResNeXt, DenseNet,
        SEResNet, SEResNeXt, Res2Net, Res2NeXt,
        SqueezeNet, MobileNetv1, MobileNetv2, MobileNetv3, MNASNet,
        EfficientNet, EfficientNetv2, ConvMixer, ConvNeXt,
-       MLPMixer, ResMLP, gMLP, ViT, UNet
+       MLPMixer, ResMLP, gMLP, ViT, UNet, ShuffleNet
 
 # useful for feature extraction
 export backbone, classifier
@@ -92,7 +95,7 @@ for T in (:AlexNet, :VGG, :SqueezeNet, :ResNet, :WideResNet, :ResNeXt,
           :Inceptionv3, :Inceptionv4, :InceptionResNetv2, :Xception,
           :MobileNetv1, :MobileNetv2, :MobileNetv3, :MNASNet,
           :EfficientNet, :EfficientNetv2, :ConvMixer, :ConvNeXt,
-          :MLPMixer, :ResMLP, :gMLP, :ViT, :UNet)
+          :MLPMixer, :ResMLP, :gMLP, :ViT, :UNet, :ShuffleNet)
     @eval Base.show(io::IO, ::MIME"text/plain", model::$T) = _maybe_big_show(io, model)
 end
 
diff --git a/src/convnets/shufflenet.jl b/src/convnets/shufflenet.jl
new file mode 100644
index 00000000..ee7fa62a
--- /dev/null
+++ b/src/convnets/shufflenet.jl
@@ -0,0 +1,200 @@
+using Flux, Metalhead, MLUtils, Functors
+
+"""
+channel_shuffle(channels, groups)
+
+Channel shuffle operation from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
+([reference](https://arxiv.org/abs/1707.01083)).
+
+# Arguments
+
+  - `channels`: number of channels
+  - `groups`: number of groups
+"""
+function channel_shuffle(x::AbstractArray{Float32, 4}, g::Int)
+    width, height, channels, batch = size(x)
+    channels_per_group = channels ÷ g
+    if channels % g == 0
+        x = reshape(x, (width, height, g, channels_per_group, batch))
+        x = permutedims(x, (1, 2, 4, 3, 5))
+        x = reshape(x, (width, height, channels, batch))
+    end
+    return x
+end
+
+"""
+ShuffleUnit(in_channels::Integer, out_channels::Integer, grps::Integer, downsample::Bool, ignore_group::Bool)
+
+Shuffle Unit from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
+([reference](https://arxiv.org/abs/1707.01083)).
+
+# Arguments
+
+  - `in_channels`: number of input channels
+  - `out_channels`: number of output channels
+  - `groups`: number of groups
+  - `downsample`: apply downsaple if true
+  - `ignore_group`: ignore group convolution if true
+"""
+function ShuffleUnit(in_channels::Integer, out_channels::Integer,
+        groups::Integer, downsample::Bool, ignore_group::Bool)
+    mid_channels = out_channels ÷ 4
+    groups = ignore_group ? 1 : groups
+    strd = downsample ? 2 : 1
+
+    if downsample
+        out_channels -= in_channels
+    end
+
+    m = Chain(Conv((1, 1), in_channels => mid_channels; groups, pad = SamePad()),
+        BatchNorm(mid_channels, NNlib.relu),
+        Base.Fix2(channel_shuffle, groups),
+        DepthwiseConv((3, 3), mid_channels => mid_channels;
+            bias = false, stride = strd, pad = SamePad()),
+        BatchNorm(mid_channels, NNlib.relu),
+        Conv((1, 1), mid_channels => out_channels; groups, pad = SamePad()),
+        BatchNorm(out_channels, NNlib.relu))
+
+    if downsample
+        m = Parallel(
+            Metalhead.cat_channels, m, MeanPool((3, 3); pad = SamePad(), stride = 2))
+    else
+        m = SkipConnection(m, +)
+    end
+    return m
+end
+
+"""
+create_shufflenet(channels, init_block_channels::Integer, groups, num_classes; in_channels=3)
+
+ShuffleNet model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
+([reference](https://arxiv.org/abs/1707.01083)).
+
+# Arguments
+
+  - `channels`: list of channels per layer
+  - `init_block_channels`: number of output channels from the first layer
+  - `groups`: number of groups
+  - `num_classes`: number of classes
+  - `in_channels`: number of input channels
+"""
+function create_shufflenet(
+        channels, init_block_channels::Integer, groups::Integer,
+        num_classes::Integer; in_channels::Integer = 3)
+    features = []
+
+    append!(features,
+        [Conv((3, 3), in_channels => init_block_channels; stride = 2, pad = SamePad()),
+            BatchNorm(init_block_channels, NNlib.relu),
+            MaxPool((3, 3); stride = 2, pad = SamePad())])
+
+    in_channels::Integer = init_block_channels
+
+    for (i, num_channels) in enumerate(channels)
+        stage = []
+        for (j, out_channels) in enumerate(num_channels)
+            downsample = j == 1
+            ignore_group = i == 1 && j == 1
+            out_ch::Integer = trunc(out_channels)
+            push!(stage, ShuffleUnit(in_channels, out_ch, groups, downsample, ignore_group))
+            in_channels = out_ch
+        end
+        append!(features, stage)
+    end
+
+    model = Chain(features...)
+    classifier = Chain(GlobalMeanPool(), MLUtils.flatten, Dense(in_channels => num_classes))
+
+    return Chain(model, classifier)
+end
+
+"""
+shufflenet(groups, width_scale, num_classes; in_channels=3)
+
+Create a ShuffleNet model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
+([reference](https://arxiv.org/abs/1707.01083)).
+
+# Arguments
+
+  - `groups`: number of groups
+  - `width_scale`: scaling factor for number of channels
+  - `num_classes`: number of classes
+  - `in_channels`: number of input channels
+"""
+
+function shufflenet(groups::Integer = 1, width_scale::Real = 1;
+        num_classes::Integer = 1000, in_channels::Integer = 3)
+    init_block_channels = 24
+    nlayers = [4, 8, 4]
+
+    if groups == 1
+        channels_per_layers = [144, 288, 576]
+    elseif groups == 2
+        channels_per_layers = [200, 400, 800]
+    elseif groups == 3
+        channels_per_layers = [240, 480, 960]
+    elseif groups == 4
+        channels_per_layers = [272, 544, 1088]
+    elseif groups == 8
+        channels_per_layers = [384, 768, 1536]
+    else
+        return error("The number of groups is not supported. Groups = ", groups)
+    end
+
+    channels = []
+    for i in eachindex(nlayers)
+        char = [channels_per_layers[i]]
+        new = repeat(char, nlayers[i])
+        push!(channels, new)
+    end
+
+    if width_scale != 1.0
+        channels = channels * width_scale
+
+        init_block_channels::Integer = trunc(init_block_channels * width_scale)
+    end
+
+    net = create_shufflenet(
+        channels,
+        init_block_channels,
+        groups,
+        num_classes;
+        in_channels)
+
+    return net
+end
+
+"""
+ShuffleNet(groups, width_scale, num_classes; in_channels=3)
+
+Create a ShuffleNet model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
+([reference](https://arxiv.org/abs/1707.01083)).
+
+# Arguments
+
+  - `groups`: number of groups
+  - `width_scale`: scaling factor for number of channels
+  - `num_classes`: number of classes
+  - `in_channels`: number of input channels
+"""
+struct ShuffleNet
+    layers::Any
+end
+@functor ShuffleNet
+
+function ShuffleNet(groups::Integer = 1, width_scale::Real = 1;
+        num_classes::Integer = 1000, in_channels::Integer = 3)
+    layers = shufflenet(groups, width_scale; num_classes, in_channels)
+    model = ShuffleNet(layers)
+
+    return model
+end
+
+(m::ShuffleNet)(x) = m.layers(x)
+
+backbone(m::ShuffleNet) = m.layers[1]
+classifier(m::ShuffleNet) = m.layers[2:end]
+
+im = rand32(224, 224, 3, 50); # a batch of 50 RGB images
+m = ShuffleNet(1, 1;num_classes=10)
+println(m(im) |> size)
\ No newline at end of file
diff --git a/test/convnet_tests.jl b/test/convnet_tests.jl
index 630de19e..9eaace6d 100644
--- a/test/convnet_tests.jl
+++ b/test/convnet_tests.jl
@@ -40,13 +40,13 @@ end
                 [2, 2, 2, 2],
                 [3, 4, 6, 3],
                 [3, 4, 23, 3],
-                [3, 8, 36, 3],
+                [3, 8, 36, 3]
             ]
             @testset for layers in layer_list
                 drop_list = [
                     (dropout_prob = 0.1, stochastic_depth_prob = 0.1, dropblock_prob = 0.1),
                     (dropout_prob = 0.5, stochastic_depth_prob = 0.5, dropblock_prob = 0.5),
-                    (dropout_prob = 0.8, stochastic_depth_prob = 0.8, dropblock_prob = 0.8),
+                    (dropout_prob = 0.8, stochastic_depth_prob = 0.8, dropblock_prob = 0.8)
                 ]
                 @testset for drop_probs in drop_list
                     m = Metalhead.resnet(block_fn, layers; drop_probs...) |> gpu
@@ -374,3 +374,15 @@ end
     @test size(model(x_256)) == (256, 256, 3, 1)
     _gc()
 end
+
+@testitem "ShuffleNet" setup=[TestModels] begin
+    configs = TEST_FAST ? [(1, 1)] :
+              [(1, 1), (2, 1), (3, 1), (4, 1), (8, 1), (1, 0.75),
+        (3, 0.75), (1, 0.5), (3, 0.5), (1, 0.25), (3, 0.25)]
+    @testset for (groups, width_scale) in configs
+        m = ShuffleNet(groups, width_scale) |> gpu
+        @test size(m(x_224)) == (1000, 1)
+        @test gradtest(m, x_224)
+        _gc()
+    end
+end