Update README.md

README.md

@@ -1,3 +1,64 @@
 ---
-license: mit
+license: apache-2.0
+tags:
+- mlx
+- mlx-image
+- vision
+- image-classification
+datasets:
+- imagenet-1k
+library_name: mlx-image
+
 ---
+
+# ResNet101
+
+ResNet101 is a computer vision model trained on imagenet-1k. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) and first released in [this repository](https://github.com/KaimingHe/deep-residual-networks). 
+
+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("resnet101")
+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("resnet101", num_classes=0)
+model.eval()
+
+embeds = model(x)
+
+# second option
+model = create_model("resnet101")
+model.eval()
+
+embeds = model.features(x)
+```