mewc_pretrained

Model Description

This is a family of pretrained Keras 3 models, based on the kimm (https://github.com/james77777778/keras-image-models) implementation of different-sized EfficientNetV2, ConvNeXt and ViT architectures, with a model top consisting of two dropout layers, a sandwiched dense compression layer, and a final classification layer. There are 10 classes, represented by an integer and common name, as specified in class_list.yaml. An example configuration file for integration with EcoAssist (https://github.com/PetervanLunteren/EcoAssist) is also provided.

Usage

The model is intended to be used within the MEWC framework, described here: https://ecoevorxiv.org/repository/view/6405/

Title A user-friendly AI workflow for customised wildlife-image classification

Authors Barry W. Brook , Jessie C. Buettel, Peter van Lunteren, Prakash P. Rajmohan & R. Zach Aandahl

Abstract Monitoring wildlife is crucial for making informed conservation and land-management decisions. Remotely triggered cameras are widely used for this, but the resulting 'big data' are laborious to process. Artificial intelligence (AI) offers a solution to this bottleneck, but it has been challenging for ecologists and practitioners to tailor current approaches to their specific use cases. Generic, online offerings also have issues of ongoing costs and data privacy. Here we present an open-source, scalable, modular, cross-platform workflow, deployed using Docker, which leverages deep learning for wildlife-image classification. Run via a user-friendly command-line interface, our workflow democratises the implementation of AI for wildlife-image classification enabling end-users without specialised technical expertise to execute a full range of tasks—from animal detection to species prediction—on local or cloud GPU-accelerated machines. It integrates seamlessly with the widely used open-source camera-trapping software ‘Camelot’, writing AI-classification data directly to image metadata and to CSV files, ready for either expert verification or direct data analysis. The end result is an advanced but accessible pipeline for wildlife-image classification. A case study with Tasmanian wildlife demonstrates the utility of our end-to-end pipeline, from classifier training to inference.

DOI https://doi.org/10.32942/X2ZW3D