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---
license: apache-2.0
library_name: mlx-image
tags:
- mlx
- mlx-image
- vision
- image-classification
datasets:
- imagenet-1k
---
# efficientnet_b1
An EfficientNet B1 model architecture, pretrained on ImageNet-1K.
Disclaimer: this is a port of the Torchvision model weights to Apple MLX Framework.
See [mlx-convert-scripts](https://github.com/lextoumbourou/mlx-convert-scripts) repo for the conversion script used.
## How to use
```bash
pip install mlx-image
```
Here is how to use this model for image classification:
```python
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=240)
x = transform(read_rgb("cat.jpg"))
x = mx.array(x)
x = mx.expand_dims(x, 0)
model = create_model("efficientnet_b1")
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=240)
x = transform(read_rgb("cat.jpg"))
x = mx.array(x)
x = mx.expand_dims(x, 0)
# first option
model = create_model("efficientnet_b1", num_classes=0)
model.eval()
embeds = model(x)
# second option
model = create_model("efficientnet_b1")
model.eval()
embeds = model.get_features(x)
```
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