Classifier & dataset for common bird species in China

Determine the 🐦 from its 🎵

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

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!

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

Galeria online de fotos de aves, com design inspirado em cartões. Explore e aprecie a beleza das aves de forma intuitiva e organizada.

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.

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.

bird_classification web application

Free open information (CC0) about nature on planet earth

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!

Bird Species Classification using Inception-v3 Network

This project is a bird classifier that uses the PyTorch framework and the ResNet50 model. It can recognize the species of birds in images based on their visual features. It supports 200 different categories of birds.

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

Selected Topics in Visual Recognition using Deep Learning, NYCU. CodaLab competition - Bird images classification

Bird Sound Recognize

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

Applications to identify birds based on their appearance and taxonomy

Signature Work @ DKU: Large Scale Bird Sound Recognition in China Region