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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: mlx-image
+tags:
+- mlx
+- mlx-image
+- vision
+- image-classification
+datasets:
+- imagenet-1k
+---
+
+# efficientnet_b2
+
+An EfficientNet B2 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=288)
+x = transform(read_rgb("cat.jpg"))
+x = mx.array(x)
+x = mx.expand_dims(x, 0)
+
+model = create_model("efficientnet_b2")
+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=288)
+x = transform(read_rgb("cat.jpg"))
+x = mx.array(x)
+x = mx.expand_dims(x, 0)
+
+# first option
+model = create_model("efficientnet_b2", num_classes=0)
+model.eval()
+
+embeds = model(x)
+
+# second option
+model = create_model("efficientnet_b2")
+model.eval()
+
+embeds = model.get_features(x)
+```

model.safetensors

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