Update README.md

README.md

@@ -1,3 +1,67 @@
 ---
-license: mit
+license: apache-2.0
+tags:
+- mlx
+- mlx-image
+- vision
+- image-classification
+datasets:
+- imagenet-1k
+library_name: mlx-image
 ---
+# vit_base_patch16_384.swag_e2e
+
+A [Vision Transformer](https://arxiv.org/abs/2010.11929v2) image classification model. Weights are learned with [SWAG](https://arxiv.org/abs/2201.08371) on ImageNet-1k data.
+
+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("vit_base_patch16_384.swag_e2e")
+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=224)
+x = transform(read_rgb("cat.png"))
+x = mx.expand_dims(x, 0)
+
+# first option
+model = create_model("vit_base_patch16_384.swag_e2e", num_classes=0)
+model.eval()
+
+embeds = model(x)
+
+# second option
+model = create_model("vit_base_patch16_384.swag_e2e")
+model.eval()
+
+embeds = model.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).