Update README.md

README.md

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-# The fine-tuned ViT model that beats [Google's state-of-the-art model](https://huggingface.co/google/vit-base-patch16-224) and OpenAI's famous GPT4
+# The fine-tuned ViT model that beats [Google's state-of-the-art model](https://huggingface.co/google/vit-base-patch16-224) and OpenAI's famous GPT4 for maps of cities around the world
 
 Image-classification fine-tuned model that identifies which city map is illustrated from an image input.
 
 The Vision Transformer (ViT) base model is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
 
+- **Developed by:** STEM.AI
+- **Model type:** Image classification of maps of cities
+- **Finetuned from model:** [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)
 
 
 ### How to use:
 
 [Inference script](https://github.com/STEM-ai/Vision/blob/7d92c8daa388eb74e8c336f2d0d3942722fec3c6/ViT_inference.py)
 
-For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#).
+For more code examples, we refer to [ViTdocumentation](https://huggingface.co/transformers/model_doc/vit.html#).
 
 ## Training data