wide_resnet101_2-mlxim

Wide ResNet101 2

WideResNet101 2 is a computer vision model trained on imagenet-1k representing an improvement of ResNet architecture. It was introduced in the paper Wide Residual Networks.

Disclaimer: This is a porting of the torchvision model weights to Apple MLX Framework.

How to use

pip install mlx-image

Here is how to use this model for image classification:

from mlxim.model import create_model
from mlxim.io import read_rgb
from mlxim.transform import ImageNetTransform

transform = ImageNetTransform(train=False, img_size=224)
x = transform(read_rgb("cat.png"))
x = mx.expand_dims(x, 0)

model = create_model("resnet18")
model.eval()

logits = model(x)

You can also use the embeds from last conv layer:

from mlxim.model import create_model
from mlxim.io import read_rgb
from mlxim.transform import ImageNetTransform

transform = ImageNetTransform(train=False, img_size=224)
x = transform(read_rgb("cat.png"))
x = mx.expand_dims(x, 0)

# first option
model = create_model("wide_resnet101_2", num_classes=0)
model.eval()

embeds = model(x)

# second option
model = create_model("wide_resnet101_2")
model.eval()

embeds = model.get_features(x)

## Model Comparison

Explore the metrics of this model in mlx-image model results.