metadata
license: apache-2.0
tags:
- mlx
- mlx-image
- vision
- image-classification
datasets:
- imagenet-1k
library_name: mlx-image
WideResNet50 2
WideResNet50 2 is a computer vision model trained on imagenet-1k representing an improvement of ResNet architecture. It was introduced in the paper Wide Residual Networks.
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=224)
x = transform(read_rgb("cat.png"))
x = mx.expand_dims(x, 0)
model = create_model("resnet18")
model.eval()
logits = model(x)
You can also use the embeds from last conv layer:
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("resnet18", num_classes=0)
model.eval()
embeds = model(x)
# second option
model = create_model("resnet18")
model.eval()
embeds = model.features(x)