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.
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