Recently, Google announced the open-sourcing of SpeciesNet, an AI model designed to identify animal species from camera trap photos. While camera traps (digital cameras connected to infrared sensors) are invaluable for wildlife research worldwide, they generate massive datasets that can take days or weeks to process.
To address this, Google launched the Wildlife Insights project six years ago as part of its Google Earth Outreach philanthropic initiative. This platform allows researchers to share, identify, and analyze wildlife images online, significantly speeding up the processing of camera trap data.
SpeciesNet is the core of this platform. Google states that the model was trained on 65 million publicly available images, supplemented by images from organizations like the Smithsonian Conservation Biology Institute, the Wildlife Conservation Society, the North Carolina Museum of Natural Sciences, and the Zoological Society of London. SpeciesNet can classify images into over 2,000 labels, encompassing animal species, broader categories like "mammal" or "felidae," and non-animal objects (e.g., "vehicle").
Google's blog post highlights that SpeciesNet's release will empower developers, academics, and biodiversity-focused startups to better monitor biodiversity in natural areas. SpeciesNet is now open-sourced on GitHub under the Apache 2.0 license, meaning it's commercially usable with minimal restrictions.
It's worth noting that Google isn't the only company offering open-source tools for automated camera trap image analysis. Microsoft's AI for Good Lab also maintains PyTorch Wildlife, an AI framework with fine-tuned pre-trained models focused on animal detection and classification.
Project: https://github.com/google/cameratrapai
Key Highlights:
🐾 Google open-sources the SpeciesNet AI model to aid wildlife identification and improve data processing efficiency.
🌍 SpeciesNet was trained on 65 million images and can identify over 2,000 animal and object labels.
🛠️ The model is open-sourced on GitHub, allowing commercial use and promoting biodiversity monitoring.
