Bird-Species-Identification 🦜🔍

Bird Species Identification is an end-to-end CNN Image Classification Model which identifies the bird species in an image. It can identify over 275 different bird species.

It is based upon pre-trained Image Classification Models that comes with Keras and then retrained on the Bird Species Dataset.

I've trained the model on 4 different CNN Architectures to get an overview of how each network performs.

Model : EfficientNetB1 Accuracy : 95.56%

Model : InceptionNetV3 Accuracy : 95.20%

Model : ResNet50 Accuracy : 96.58%

Model : MobileNetV2 Accuracy : 95.13%

After training the model, I've exported it in .hfd5 format and then integrated it with the streamlit Web Framework

Streamlit is an open-source app framework that turns data scripts into shareable web apps in minutes.

Once I got the App running on my local environment, I then deployed the App on the Heroku platform.

To view the Deployed app Click here

The app may take a couple of seconds to load for the first time, but it works perfectly fine.

If you want to dive deeper on how the model was trained check out transfer-learning-model-training.ipynb Notebook

Breaking down the repo

• .gitignore : Tells which files/folders to ignore while tracking
• .slugignore : Contains which files to be removed after you push code to Heroku and before the buildpack runs.
• app.py : Contains web app code built using streamlit api
• utils.py : Some of used fuctions in app.py
• transfer_learning_model_training.ipynb : Jupyter Notebook used to train and evaluate Models
• Models : Contains all the models in .hfd5 format
• requirements.txt : List of required dependencies