metadata

license: apache-2.0
library_name: mlx-image
tags:
  - mlx
  - mlx-image
  - vision
  - image-classification
datasets:
  - imagenet-1k

regnet_x_1_6gf

A RegNetX-1.6GF image classification model. Pretrained in ImageNet by torchvision contributors (see ImageNet1K-V2 weight details https://github.com/pytorch/vision/issues/3995#new-recipe).

Disclaimer: This is a porting of the torch 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("regnet_x_1_6gf")
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=224)
x = transform(read_rgb("cat.png"))
x = mx.expand_dims(x, 0)

# first option
model = create_model("regnet_x_1_6gf", num_classes=0)
model.eval()

embeds = model(x)

# second option
model = create_model("regnet_x_1_6gf")
model.eval()

embeds = model.get_features(x)