This is my bird species classification project with complete UI and map, for project showcase at my college.
DATASET LINK: https://www.kaggle.com/datasets/gpiosenka/100-bird-species
PS: We won the third position among the 70+projects In AI/ML Category
Welcome to the Bird Classifier project!
The Bird Classifier is a web application that uses machine learning to classify over 450 different bird species. It is built using Python and several machine learning libraries, including TensorFlow and Keras.
The application has a user-friendly interface where users can upload images of birds they would like to identify. The image is then passed through the pre-trained convolutional neural network, which has been trained on a dataset of over 100,000 bird images. Once the image is classified, the application displays the name of the bird species along with a brief description and location information.
In addition to bird classification, the application also utilizes several APIs to display location data and additional information about the identified bird species. The application pulls data from the eBird API to display a map with the bird's reported sightings and the Bird API to display additional information such as bird calls and natural history.
The Bird Classifier project is a great tool for bird enthusiasts, researchers, and anyone interested in learning more about the diversity of bird species. We hope you enjoy using the application as much as we enjoyed building it!
To get started with the Bird Classifier, simply clone the repository and follow the instructions in the README file.
- Installation
- Usage
- About Model
- eBird API
Clone the repository to your local machine. Install the required dependencies by running the following command: pip install -r requirements.txt
- Download the zip file
- Create a python env
- Install requirement.txt
- Run main.py
- Select Image Of Your Choice And Upload It On The Web Page
- Correct Classfier, Info And Top 5 Location Of That Bird Will Be Displayed!
A fine-tuned MobileNet model is a MobileNet model that has been trained on a bird species dataset using hyperparameter optimization (HPO) tuning to improve its performance on bird species classification.
The eBird API is a web service provided by the Cornell Lab of Ornithology that allows developers to programmatically access data from the eBird database. The eBird database is a vast collection of bird observations from all over the world, contributed by birdwatchers, scientists, and other members of the public.