Update README.md

README.md

@@ -9,16 +9,25 @@ Automatic detection of blast cells in ALL data using transformers.
 Official implementation of our work: *"Automated Identification of Cell Populations in Flow Cytometry Data with Transformers"*
 by Matthias Wödlinger, Michael Reiter, Lisa Weijler, Margarita Maurer-Granofszky, Angela Schumich, Elisa O Sajaroff, Stefanie Groeneveld-Krentz, Jorge G Rossi, Leonid Karawajew, Richard Ratei and Michael Dworzak
 
-## Usage
+## Load the model
 
 Load the pretrained model from huggingface
 
-```
+```python
 from transformers import AutoModel
 flowformer = AutoModel.from_pretrained("matth/flowformer", trust_remote_code=True)
 ```
 
-`trust_remote_code=True` is necessary because the model code uses a custom architecture. 
+`trust_remote_code=True` is necessary because the model code uses a custom architecture.
+
+## Usage
+
+The model expects as input a pytorch tensor `x` with shape `batch_size x num_cells x num_markers`. 
+The pretrained model is trained with the the markers: *TIME, FSC-A, FSC-W, SSC-A, CD20, CD10, CD45, CD34, CD19, CD38, SY41*. If you use different markers (or a different ordering of markers), you need to specify this by setting the `markers` kwarg in the model forward pass:
+
+```python
+output = flowformer(x, markers=["Marker1", "Marker2", "Marker3"])
+```
 
 ## Citation