Using convolutional neural networks to build and train a bird species classifier on bird song data with corresponding species labels.

Polish bird species recognition - Bird song analysis and classification with MFCC and CNNs. Trained on EfficientNets with final score 0.88 AUC. Women in Machine Learning & Data Science project.

Supervised Classification of bird species 🐦 in high resolution images, especially for, Himalayan birds, having diverse species with fairly low amount of labelled data [ICVGIPW'18]

A repo designed to convert audio-based "weak" labels to "strong" intraclip labels. Provides a pipeline to compare automated moment-to-moment labels to human labels. Methods range from DSP based foreground-background separation, cross-correlation based template matching, as well as bird presence sound event detection deep learning models!

Code for searching the www.xeno-canto.org bird sound database, and training a machine learning model to classify birds according to their sounds.

Engineered a robust deep learning model using Convolutional Neural Networks and TensorFlow to classify 114 bird species based on audio recordings. Model achieved an impressive accuracy of 93.4%, providing valuable insights for conservationists and ecologists in the wildlife & ecological research sectors.

Computer vision website which recognizes and provides information about birds in user-uploaded photos.

Bird Classifier developped in tensorflow using pre-trained model from Tensorflow Hub and running on Google Colab

Explores jigsaw puzzles solvinig as pre-text task for fine grained classification for bird species identification (Implemented with pyTorch)

Southern African Bird Call Audio Identification Challenge

Classifies a bird's species using a neural network in tensorflow..

New is not always better: a comparison of two image classification networks (ResNet-50 vs ConvNeXt).

ResNet-34 Model trained from scratch to classify 450 different species of birds with 98.6% accuracy.

Source code for BMBF InnoTruck demo of BirdNET.

Code used for my final project in Computer Vision at Texas State University, Spring 2019

Explore deep learning-powered image classification with PyTorch. Achieved 98% accuracy on Natural Images and 95% on Birds Species using AlexNet and EfficientNet-B1. Dive into the code and results!

BirdNET as a systemd service with other features.

This project aims to detect bird species using a Convolutional Neural Network (CNN). The model was trained on six categories, including five bird species and one category for 'no bird detected'. The project includes resources for training the model and using it for detection and species recognition.

Classifier & dataset for common bird species in China

Polish bird species recognition - Bird song analysis and classification. Women in Machine Learning & Data Science project.