metadata
license: apache-2.0
library_name: mlx-image
tags:
- mlx
- mlx-image
- vision
- image-classification
datasets:
- imagenet-1k
regnet_y_3_2gf
A RegNetY-3.2GF 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_y_3_2gf")
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_y_3_2gf", num_classes=0)
model.eval()
embeds = model(x)
# second option
model = create_model("regnet_y_3_2gf")
model.eval()
embeds = model.get_features(x)