Update README.md

README.md

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 tags:
 - fastai
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-# Amazing!
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-🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
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-# Some next steps
-1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
-
-2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
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-3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
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-Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
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@@ -23,10 +8,10 @@ Greetings fellow fastlearner 🤝! Don't forget to delete this content from your
 # Model card
 
 ## Model description
-More information needed
+A neural network model trained with fastai and timm to classify 400 bird species in an image. 
 
 ## Intended uses & limitations
-More information needed
+This bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classification which will output a class even if there is no bird in the image. 
 
 ## Training and evaluation data
-More information needed
+Pre-trained model used is Efficient net from timm library, specifically *efficientnet_b3a*. The dataset trained is from Kaggle [BIRDS 400 - SPECIES IMAGE CLASSIFICATION](https://www.kaggle.com/datasets/gpiosenka/100-bird-species). Evaluation accuracy score on the given test set from Kaggle is 99.4%. Please note this is likely not representative of real world performance, as mentioned by dataset provider that the test set is hand picked as the best images.