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

---

# Wide ResNet101 2

WideResNet101 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](https://arxiv.org/abs/1605.07146). 

Disclaimer: This is a porting of the torchvision model weights to Apple MLX Framework.


## How to use
```bash
pip install mlx-image
```

Here is how to use this model for image classification:

```python
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:
```python
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("wide_resnet101_2", num_classes=0)
model.eval()

embeds = model(x)

# second option
model = create_model("wide_resnet101_2")
model.eval()

embeds = model.get_features(x)
```

## Model Comparison

Explore the metrics of this model in [mlx-image model results](https://github.com/riccardomusmeci/mlx-image/blob/main/results/results-imagenet-1k.csv).