Acoustic mosquito detection code with Bayesian Neural Networks

Last update: Nov 28, 2022

Overview

HumBugDB

Acoustic mosquito detection with Bayesian Neural Networks.

Extract audio or features from our large-scale dataset on Zenodo.
This repository outlines two key example use cases for the data:
- Task 1: Mosquito Event Detection (MED)
- Task 2: Mosquito Species Classification (MSC)

General use instructions

This code is complementary to the paper: "HumBugDB: a large-scale acoustic mosquito dataset" and the dataset on Zenodo.

See documentation in the paper supplement for:

Section A: Licensing
Section B: Code use, feature and model engineering
Section C: Description and visualisations of metadata in data/metadata/*.csv

Additional documentation for:

Code, feature, hyperparameter configuration

Installation instructions

You may choose to use the Colab environment which is natively compatible with all of our code. Alternatively, see the instructions for manually configuring an environment to run the Jupyter notebooks.

Google Colab

Installation and use with Google Colab here.

Jupyter notebook

Installation instructions and requirements for PyTorch: InstallationLogPyTorch.txt.
Keras min. requirements: condarequirementsKeras.txt and piprequirementsKeras.txt. Compatible with Tensorflow 1.X and 2.X.

After installation of requirements:

git clone https://github.com/HumBug-Mosquito/HumBugDB.git

Extract audio from four-part-archive to Zenodo to /data/audio/.

Contact

Developed by Ivan Kiskin of MLRG University of Oxford. Contact [email protected] for enquiries or suggestions. Follow our Twitter on @OxHumBug and visit our HumBug website for updates on the overall HumBug project.

Bayesian Neural Networks in PyTorch

We present the new scheme to compute Monte Carlo estimator in Bayesian VI settings with almost no memory cost in GPU, regardles of the number of sampl

7 May 3, 2022

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

1 Jan 1, 2022

Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

1 Nov 12, 2021

Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit

2.2k Jan 3, 2023

This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

The Neural Process Family This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CN

892 Dec 28, 2022

Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20

161 Dec 20, 2022

Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"

Dangers of Bayesian Model Averaging under Covariate Shift This repository contains the code to reproduce the experiments in the paper Dangers of Bayes

25 Sep 21, 2022

Companion code for the paper "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence" (NeurIPS 2021)

ReLU-GP Residual (RGPR) This repository contains code for reproducing the following NeurIPS 2021 paper: @inproceedings{kristiadi2021infinite, title=

4 Dec 26, 2021

Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo"

dblmahmc Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo" Requirements: https://github.com

1 Dec 17, 2021

Releases(2.0)

2.0(Sep 1, 2021)
HumBugDB models

This release distributes the binaries for the Bayesian neural network models, to preserve the small size of the repository.

We include models trained for two tasks. The model objects and tasks are described at:

MED: Mosquito Event detection

MSC: Mosquito Species classification

The models correspond to all of the results reported in the paper HumBugDB: A Large-scale Acoustic Mosquito Dataset.
Source code(tar.gz)
Source code(zip)
MED_trained_models.zip(608.00 MB)
MSC_trained_models_1.zip(1280.73 MB)
MSC_trained_models_2.zip(1736.36 MB)
v1.0(Jun 9, 2021)
This release distributes the binaries for the Bayesian neural network models, to preserve the small size of the repository. We include five trained models, to be placed in /outputs/models/keras/ or /outputs/models/pytorch/:

PyTorch BNN-ResNet-50 (paper version): neurips_2021_humbugdb_resnet50_bnn.pth

BNN-ResNet-18 (paper version): neurips_2021_humbugdb_resnet18_bnn.pth

Keras BNN-4conv (paper version): neurips_2021_humbugdb_keras_bnn.hdf5

Keras BNN-4conv (best version): neurips_2021_humbugdb_keras_bnn_best.hdf5

Keras BNN-4conv (for appendix Test B): neurips_2021_humbugdb_supplement_TestB_keras_bnn.hdf5

Source code(tar.gz)
Source code(zip)
neurips_2021_humbugdb_keras_bnn.hdf5(6.00 MB)
neurips_2021_humbugdb_keras_bnn_best.hdf5(5.98 MB)
neurips_2021_humbugdb_resnet18_bnn.pth(42.71 MB)
neurips_2021_humbugdb_resnet50_bnn.pth(89.99 MB)
neurips_2021_humbugdb_supplement_TestB_keras_bnn.hdf5(5.98 MB)

Owner

GitHub Repository

Computer Vision application in the web

Computer Vision application in the web Preview Usage Clone this repo git clone https://github.com/amineHY/WebApp-Computer-Vision-streamlit.git cd Web

35 Dec 06, 2022

[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".

CORE This is the official PyTorch implementation for the paper: Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao. CORE: Simple and Effective Sess

26 Dec 19, 2022

Cossim - Sharpened Cosine Distance implementation in PyTorch

Sharpened Cosine Distance PyTorch implementation of the Sharpened Cosine Distanc

10 Mar 22, 2022

Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.

One Thing One Click One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation (CVPR2021) Code for the paper One Thi

44 Dec 12, 2022

Unsupervised Representation Learning via Neural Activation Coding

Neural Activation Coding This repository contains the code for the paper "Unsupervised Representation Learning via Neural Activation Coding" published

5 May 26, 2022

A Keras implementation of CapsNet in the paper: Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules

NOTE This implementation is fork of https://github.com/XifengGuo/CapsNet-Keras , applied to IMDB texts reviews dataset. CapsNet-Keras A Keras implemen

5 Oct 23, 2022

Acoustic mosquito detection code with Bayesian Neural Networks

Related tags

Overview

HumBugDB

General use instructions

Installation instructions

Google Colab

Jupyter notebook

Contact

You might also like...

Bayesian Neural Networks in PyTorch

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"

Companion code for the paper "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence" (NeurIPS 2021)

Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo"

Releases(2.0)

2.0(Sep 1, 2021)

HumBugDB models

v1.0(Jun 9, 2021)

Owner

Computer Vision application in the web

[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".

Cossim - Sharpened Cosine Distance implementation in PyTorch

Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.

Unsupervised Representation Learning via Neural Activation Coding

A Keras implementation of CapsNet in the paper: Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules

Learning Temporal Consistency for Low Light Video Enhancement from Single Images (CVPR2021)

QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

Code release of paper "Deep Multi-View Stereo gone wild"

Reinforcement learning algorithms in RLlib

Laplace Redux -- Effortless Bayesian Deep Learning

A collection of scripts I developed for personal and working projects.

CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation

Yas CRNN model training - Yet Another Genshin Impact Scanner

Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

An educational tool to introduce AI planning concepts using mobile manipulator robots.

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems

Simple and ready-to-use tutorials for TensorFlow

An efficient framework for reinforcement learning.