Update README.md

README.md

@@ -18,7 +18,7 @@ datasets:
 - Livingwithmachines/MapReader_Data_SIGSPATIAL_2022
 ---
 
-# Model card for mr_vit_base_patch16_224_timm_pretrain
+# Model card for mr_vit_base_patch16_224_timm_pretrain_railspace_and_building
 
 A Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. 
 Fine-tuned on gold standard annotations and outputs from early experiments using MapReader  (found [here](https://huggingface.co/datasets/Livingwithmachines/MapReader_Data_SIGSPATIAL_2022)).
@@ -30,9 +30,19 @@ Fine-tuned on gold standard annotations and outputs from early experiments using
 - **Model type:** Image classification /feature backbone
 - **Finetuned from model:** https://huggingface.co/google/vit-base-patch16-224
 
+### Classes and labels
+
+- 0: no
+- 1: railspace
+- 2: building
+- 3: railspace & building
+
 ## Uses
 
-This fine-tuned version of the model is an output of the MapReader pipeline. It was used to classify 'patch' images (cells/regions) of scanned nineteenth-century series maps of Britain provided by the National Library of Scotland (learn more [here](https://maps.nls.uk/os/)). We classified patches to indicate the presence of buildings and railway infrastructure. See [our paper](https://dl.acm.org/doi/10.1145/3557919.3565812) for more details about labels.
+This fine-tuned version of the model is an output of the MapReader pipeline.
+It was used to classify 'patch' images (cells/regions) of scanned nineteenth-century series maps of Britain provided by the National Library of Scotland (learn more [here](https://maps.nls.uk/os/)). 
+We classified patches to indicate the presence of buildings and railway infrastructure. 
+See [our paper](https://dl.acm.org/doi/10.1145/3557919.3565812) for more details about labels.
 
 ## How to Get Started with the Model in MapReader