Update README.md

README.md

@@ -18,7 +18,7 @@ datasets:
 - Livingwithmachines/MapReader_Data_SIGSPATIAL_2022
 ---
 
-# Model Card for mr_resnest101e_timm_pretrain
+# Model Card for mr_resnest101e_timm_pretrain_railspace_and_building
 
 A ResNeSt (ResNet based architecture with Split Attention) image classification model. 
 Trained on ImageNet-1k and fine-tuned on gold standard annotations and outputs from early experiments using MapReader  (found [here](https://huggingface.co/datasets/Livingwithmachines/MapReader_Data_SIGSPATIAL_2022)).
@@ -30,11 +30,21 @@ Trained on ImageNet-1k and fine-tuned on gold standard annotations and outputs f
 - **Model type:** Image classification /feature backbone
 - **Finetuned from model:**  https://huggingface.co/timm/resnest101e.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 ACM SIGSPATIAL Geohumanities Workshop 2022 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 ACM SIGSPATIAL Geohumanities Workshop 2022 paper](https://dl.acm.org/doi/10.1145/3557919.3565812) for more details about labels.
 
-Of all the models fine-tuned for the experiments reported in our ACM SIGSPATIAL 2022 paper, we used this resnext101e_timm_pretrain model to infer these railway infrastructure and building labels across 30 million patches on nearly 16k scanned map sheets. You can visualize the results of this work [here](https://maps.nls.uk/projects/mapreader/#zoom=6.0&lat=56.00000&lon=-4.00000).
+Of all the models fine-tuned for the experiments reported in our ACM SIGSPATIAL 2022 paper, we used this resnest101e_timm_pretrain model to infer these railway infrastructure and building labels across 30 million patches on nearly 16k scanned map sheets. You can visualize the results of this work [here](https://maps.nls.uk/projects/mapreader/#zoom=6.0&lat=56.00000&lon=-4.00000).
 
 ## How to Get Started with the Model in MapReader