Added model card

README.md

@@ -28,7 +28,7 @@ The objective is to detect and classify cells of different tissues. Different mo
 
 ## Model description
 
-The model is made by [Hovernet](https://github.com/vqdang/hover_net) as a backbone and a graph neural network on top to improve the classification step. Each backbone comes trained at two resolutions: 270x270 and 518x518. They also come in two version each, trained from scratch of fine-tuned from the consep checkpoint of Hovernet (FT). Then, for each Hovernet model, five graph neural networks. Four graph convolutional neural networks trained with different sets of features and one graph attention network trained with all the features.
+The model is made by [Hovernet](https://github.com/vqdang/hover_net) as a backbone and a graph neural network on top to improve the classification step. Each backbone comes trained at two resolutions: 270x270 and 518x518. They also come in two version each, trained from scratch or fine-tuned from the consep checkpoint of Hovernet (FT). Then, for each Hovernet model, five graph neural networks are provided that can be used on top. Four graph convolutional neural networks trained with different sets of features and one graph attention network trained with all the features.
 
 To use the models the tumourkit library comes with a simple [demo](https://lung-tumour-study.readthedocs.io/en/latest/usage.html#gradio-demo) that you can try. Beware, on CPU it takes nearly 10 minutes per 1024x1024 image.