import mlx.core as mx
from mlxim.model import create_model
from mlxim.io import read_rgb
from mlxim.transform import ImageNetTransform
from mlxim.utils.imagenet import IMAGENET2012_CLASSES

transform = ImageNetTransform(train=False, img_size=380)
x = transform(read_rgb("cat.jpg"))
x = mx.array(x)
x = mx.expand_dims(x, 0)

model = create_model("efficientnet_b4")
model.eval()

logits = model(x)
predicted_idx = mx.argmax(logits, axis=-1).item()
predicted_class = list(IMAGENET2012_CLASSES.values())[predicted_idx]

print(f"Predicted class: {predicted_class}")

You can also use the embeds from layer before head:

python

import mlx.core as mx
from mlxim.model import create_model
from mlxim.io import read_rgb
from mlxim.transform import ImageNetTransform

transform = ImageNetTransform(train=False, img_size=380)
x = transform(read_rgb("cat.jpg"))
x = mx.array(x)
x = mx.expand_dims(x, 0)

# first option
model = create_model("efficientnet_b4", num_classes=0)
model.eval()

embeds = model(x)

# second option
model = create_model("efficientnet_b4")
model.eval()

embeds = model.get_features(x)

mlx-vision/efficientnet_b4-mlxim

efficientnet_b4

How to use

mlx-vision/
efficientnet_b4-mlxim