Update README.md

README.md

@@ -18,7 +18,7 @@ datasets:
 - Livingwithmachines/MapReader_Data_SIGSPATIAL_2022
 ---
 
-# Model Card for mr_tf_efficientnet_b3_ns_timm_pretrain
+# Model Card for mr_tf_efficientnet_b3_ns_timm_pretrain_railspace_and_building
 
 A EfficientNet image classification model. 
 Trained on ImageNet-1k and unlabeled JFT-300m using Noisy Student semi-supervised learning in Tensorflow by paper authors, ported to PyTorch by Ross Wightman. 
@@ -31,9 +31,19 @@ Fine-tuned on gold standard annotations and outputs from early experiments using
 - **Model type:** Image classification /feature backbone
 - **Finetuned from model:**  https://huggingface.co/timm/tf_efficientnet_b3.ns_jft_in1k
 
+### 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