Upload folder using huggingface_hub

README.md

@@ -1,3 +1,66 @@
----
-license: apache-2.0
----
+---
+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
+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.png"))
+x = mx.expand_dims(x, 0)
+
+model = create_model("efficientnet_b1")
+model.eval()
+
+logits = model(x)
+```
+
+You can also use the embeds from layer before head:
+
+```python
+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.png"))
+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)
+```

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b15be1fa42b15e4a4bd69eebcef8cd4b2182d28a09e093d0a51e68fb69667cd
+size 31471286