Using convolutional neural networks to build and train a bird species classifier on bird song data with corresponding species labels.
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Updated
Dec 10, 2024 - Python
Using convolutional neural networks to build and train a bird species classifier on bird song data with corresponding species labels.
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]
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.
Code for searching the www.xeno-canto.org bird sound database, and training a machine learning model to classify birds according to their sounds.
Explores jigsaw puzzles solvinig as pre-text task for fine grained classification for bird species identification (Implemented with pyTorch)
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!
BirdNET as a systemd service with other features.
ResNet-34 Model trained from scratch to classify 450 different species of birds with 98.6% accuracy.
Classifies a bird's species using a neural network in tensorflow..
Computer vision website which recognizes and provides information about birds in user-uploaded photos.
Fine-grained species classification
Source code for BMBF InnoTruck demo of BirdNET.
Bird Classifier developped in tensorflow using pre-trained model from Tensorflow Hub and running on Google Colab
Code used for my final project in Computer Vision at Texas State University, Spring 2019
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.
Southern African Bird Call Audio Identification Challenge
MVA - Kaggle Challenge - Bird Image Recognition
Signature Work @ DKU: Large Scale Bird Sound Recognition in China Region
Determine the 🐦 from its 🎵
Bird Sound Recognize
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