Update README.md

README.md

@@ -12,8 +12,6 @@ library_name: transformers
 This model performs visual feature extraction.
 For instance, RADIO generates image embeddings that can be used by a downstream model to classify images.
 
-This model is for research and development only.
-
 ### License/Terms of Use
 
 [License] This model is governed by the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf).
@@ -27,7 +25,7 @@ This model is for research and development only.
 
 ## Input:
 **Input Type(s):** Image <br>
-**Input Format(s):** Red, Green, Blue (RGB) <br>
+**Input Format(s):** Red, Green, Blue (RGB) pixel values in [0, 1] range. <br>
 **Input Parameters:** Two Dimensional (2D) <br>
 **Other Properties Related to Input:** Image resolutions up to 2048x2028 in increments of 16 pixels <br>
 
@@ -37,6 +35,42 @@ This model is for research and development only.
 **Output Parameters:** 2D <br>
 **Other Properties Related to Output:** Downstream model required to leverage image features <br>
 
+## Usage:
+
+RADIO will return a tuple with two tensors.
+The `summary` is similar to the `cls_token` in ViT and is meant to represent the general concept of the entire image.
+It has shape `(B,C)` with `B` being the batch dimension, and `C` being some number of channels.
+The `spatial_features` represent more localized content which should be suitable for dense tasks such as semantic segmentation, or for integration into an LLM.
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoModel, CLIPImageProcessor
+
+hf_repo = "nvidia/C-RADIO"
+
+image_processor = CLIPImageProcessor.from_pretrained(hf_repo)
+model = AutoModel.from_pretrained(hf_repo, trust_remote_code=True)
+model.eval().cuda()
+
+image = Image.open('./assets/radio.png').convert('RGB')
+pixel_values = image_processor(images=image, return_tensors='pt', do_resize=True).pixel_values
+pixel_values = pixel_values.cuda()
+
+summary, features = model(pixel_values)
+```
+
+Spatial features have shape `(B,T,D)` with `T` being the flattened spatial tokens, and `D` being the channels for spatial features. Note that `C!=D` in general.
+Converting to a spatial tensor format can be done using the downsampling size of the model, combined with the input tensor shape. For RADIO, the patch size is 16.
+
+```Python
+from einops import rearrange
+spatial_features = rearrange(spatial_features, 'b (h w) d -> b d h w', h=x.shape[-2] // patch_size, w=x.shape[-1] // patch_size)
+```
+
+The resulting tensor will have shape `(B,D,H,W)`, as is typically seen with computer vision models.
+
+
 ## Software Integration:
 **Runtime Engine(s):**
 * TAO- 24.10 <br>