Update README.md

Add brief description of the training data.

README.md

@@ -3,19 +3,9 @@ tags:
 - fastai
 ---
 
-# Amazing!
-
-🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
-
-# 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)).
-
-3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
-
-Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
+This model was trained to as part of collaboration between [Mote Marine Laboratory & Aquarium](https://mote.org), [Southeast Coastal Ocean Observing Regional Association](https://secoora.org), and [Axiom Data Science](https://axiomdatascience.com) to develop a model capable of detecting and classifying fish vocalizations from audio files collected from hydrophones.
 
+More information available at [the project archive repo](https://github.com/axiom-data-science/project-classify-fish-sounds).
 
 ---
 
@@ -23,10 +13,60 @@ Greetings fellow fastlearner 🤝! Don't forget to delete this content from your
 # Model card
 
 ## Model description
-More information needed
+
+This model was trained on spectrograms 
+
+A [reproducible Jupyter notebook](https://github.com/axiom-data-science/project-classify-fish-sounds/blob/main/notebooks/train-resnet101-fastai.ipynb) describing the training of the model is available in the archive repo.
 
 ## Intended uses & limitations
-More information needed
+
+The model was intended to be a proof on concept to aid researchers identify fish vocalizations through vast amounts of audio data collected from hydrophones.
+Although the training data was collected using multiple devices in multiple locations, the model may not be generally applicable to other uses.
 
 ## Training and evaluation data
-More information needed
+
+A training set of spectrograms of fish calls was created based on annotations of fish sounds in passive acoustic recordings by a hydrophone were provided by Jim Locascio, Max Fullmer, and volunteers from the [Mote Marine Laboratory & Aquarium](https://mote.org).
+
+Due to severe imbalances in the number of samples per class, the training involved both under-sampling classes with many samples and over-sampling classes with few classes so that the model was trained on 50 samples per class.  This number was derived in a completely ad-hoc fashion based on the distribution of class samples.
+
+### Class label description
+
+| Call Index | Description |
+|------------|-------------|
+|    0  |   Background noise (no fish vocalizations) |
+|    1  |   Black grouper 1 |
+|    2  |   Black grouper 2 |
+|    3  |   Black grouper grunt |
+|    4  |   Black grouper spawning rush |
+|    5  |   Black grouper chorus < 50% of file |
+|    6  |   Black grouper chrous > 50% of file |
+|    8  |   Unidentified sound type |
+|    9  |   Red grouper 1 |
+|    10 |   Red grouper 2 |
+|    17 |   Red hind 1 |
+|    18 |   Red hind 2 |
+|    19 |   Red hind 3 |
+|    25 |   Goliath grouper 1 |
+|    27 |   Multi-phase goliath grouper |
+|    28 |   Sea trout chorus |
+|    29 |   Silver perch call |
+
+### Class indices in trained model
+
+Some classes did not meet the training criteria, high signal-to-noise ratio and minimum call overlap, and were therefore excluded from the model training.
+As such, the number of classes represented in the trained model is few than the amount of labeled classes in the training set.
+
+| Call Index | Description |
+----------------------------
+|0 | No call |
+|1 | Black grouper call |
+|2 | Black grouper call 2 |
+|3 | Black grouper grunt |
+|4 | Unidentified sound |
+|5 | Red grouper 1 |
+|6 | Red grouper 2 |
+|7 | Red hind 1 |
+|8 | Red hind 2 |
+|9 | Red hind 3 |
+|10 | Goliath grouper |
+|11 | Goliath grouper multi-phase |