swin_v2_tiny_patch4_window8_256-mlxim

swin_v2_tiny_patch4_window8_256

A Swin Transformer image classification model. Weights are learned on ImageNet-1k data.

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=256)
x = transform(read_rgb("cat.png"))
x = mx.expand_dims(x, 0)

model = create_model("swin_v2_tiny_patch4_window8_256")
model.eval()

logits = model(x)

You can also use the embeds from layer before head:

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

transform = ImageNetTransform(train=False, img_size=256)
x = transform(read_rgb("cat.png"))
x = mx.expand_dims(x, 0)

# first option
model = create_model("swin_v2_tiny_patch4_window8_256", num_classes=0)
model.eval()

embeds = model(x)

# second option
model = create_model("swin_v2_tiny_patch4_window8_256")
model.eval()

embeds = model.get_features(x)

## Model Comparison

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