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A Beginner's Guide to Google SpeciesNet
Google SpeciesNet Introduction: Easy Enough for a Rabbit
Introduction
Do you think of wildlife observation and biodiversity conservation as just painstaking field observation and manual analysis? In fact, AI technology has evolved rapidly over the past few years, revolutionizing this field.
Developed by Google's AI team, "SpeciesNet" is an AI model that automatically identifies wildlife photos taken by trail cameras (motion-triggered cameras). It was open-sourced in March 2025 and is now available to everyone for free.
In this article, I will explain Google's SpeciesNet, from its basic mechanisms to actual use cases and installation methods, in a way that is easy enough for even a rabbit to understand. If you are interested in biodiversity conservation or want to know practical examples of AI technology, please hop along and read on!
SpeciesNet Basics
Development Background and Purpose
SpeciesNet was developed to streamline biodiversity conservation research. Trail cameras, which have become essential monitoring tools in ecosystems worldwide, generate vast amounts of image data. Manual processing of these images had become a major bottleneck.
Previously, experts could only process about 300 to 1,000 images per hour, but with SpeciesNet, it is now possible to automatically process as many as 3.6 million images per hour. This staggering improvement in processing speed allows researchers to focus on actual conservation activities and research rather than data processing.
Relationship with Wildlife Insights Platform
SpeciesNet has been utilized since 2019 as the core technology of "Wildlife Insights," a cloud-based platform operated by Google. This platform centrally manages image data collected by researchers worldwide and achieves automated analysis through SpeciesNet.
Wildlife Insights integrates field data accumulated over many years by world-renowned research institutions, such as the Smithsonian Conservation Biology Institute and the Zoological Society of London. SpeciesNet has been trained and its accuracy improved based on this data.
Technical Features of SpeciesNet
The most significant feature of SpeciesNet is its training dataset, consisting of over 65 million camera trap images. These images are tagged with more than 2,000 species labels, establishing a multi-layered classification system from the species level to higher taxonomic groups (such as mammals or felids).
Furthermore, by being open-sourced under the Apache 2.0 license, a wide range of applications, including commercial use, has become possible. This allows even community-led conservation projects to perform advanced AI analysis.
How SpeciesNet Works
Two-Stage Architecture
SpeciesNet employs a two-stage architecture.
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Stage 1: MegaDetector
Detects objects such as animals, humans, and vehicles within an image. This stage performs only object detection and does not identify the species. -
Stage 2: Classifier
Classifies the objects detected by MegaDetector at the species level. Using the EfficientNet V2 M architecture, it can identify not only more than 2,000 species of animals but also higher taxonomic groups (such as mammals or felids) and non-animal classes (empty, vehicle).
This two-stage configuration improves accuracy by identifying the species after first pinpointing where the animal is located in the image. Furthermore, by incorporating geographic information (country and region codes), it achieves more precise predictions by excluding species that do not inhabit the specified area.
Processing Pipeline
The SpeciesNet processing pipeline is as follows:
- Image Preprocessing: Preprocesses the original images and inputs them into MegaDetector and the classifier.
- Object Detection: MegaDetector detects animals, humans, and vehicles within the images.
- Species Classification: The classifier identifies the species of the detected objects.
- Geographic Filtering: Excludes inappropriate predictions based on geographic information.
- Result Output: Outputs the results in JSON format.
This pipeline enables high-precision and high-speed identification of wildlife. Even rabbits can be automatically identified at the species level—hop!
Performance and Features
Accuracy
The performance of SpeciesNet significantly exceeds that of conventional image recognition models:
- Animal detection accuracy: 99.4%
- Species-level identification accuracy: 83%
- Empty image exclusion accuracy: 98.6%
With this high level of accuracy, researchers can efficiently extract useful information from vast amounts of image data. In particular, the ability to exclude empty images (images with no animals present) with high precision leads to significant time savings in actual wildlife research.
Improving Accuracy Using Geographic Information
One interesting feature of SpeciesNet is that it uses geographic information to improve prediction accuracy. For example, when analyzing images from the UK, it is possible to predict species according to the region, such as by ensuring it doesn't predict a kangaroo.
This is achieved by specifying a country code (a 3-letter ISO 3166-1 alpha-3 code) or, in the case of the US, a state code (a 2-letter state abbreviation). This geographic filtering allows for the exclusion of ecologically unnatural predictions, resulting in more reliable outcomes.
Processing Speed
The incredible processing speed of SpeciesNet makes the analysis of large-scale image datasets practical. Capable of processing 3.6 million images per hour, it is possible to analyze even millions of trail camera images within a realistic timeframe.
This means, for instance, that 1 million images captured over a year by 100 camera traps installed in a protected area can now be analyzed in a few hours. Tasks that previously took several months with manual processing have been drastically shortened.
Actual Usage
Installation Method
SpeciesNet can be easily installed and used in a Python environment. Here are the basic steps:
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Set up a Python virtual environment (if you don't have a Python environment, install Python following the SpeciesNet installation instructions).
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Install the SpeciesNet package using the pip command:
pip install speciesnet -
For Mac users experiencing errors, add the following option:
pip install speciesnet --use-pep517 -
Verify the installation:
python -m speciesnet.scripts.run_model --help
Basic Usage
The simplest way to use SpeciesNet is by using the run_model script:
python -m speciesnet.scripts.run_model --folders "path/to/image/folder" --predictions_json "path/to/output/json"
When you run this command, the detector and classifier will be downloaded automatically, and the images in the specified folder will be analyzed. You can also specify geographic information:
python -m speciesnet.scripts.run_model --folders "path/to/image/folder" --predictions_json "path/to/output/json" --country JPN
In this example, the --country JPN option is added to specify that images from Japan are being analyzed. For the US, you can also specify a state code:
python -m speciesnet.scripts.run_model --folders "path/to/image/folder" --predictions_json "path/to/output/json" --country USA --admin1_region CA
In this example, it specifies that images from California, USA, are being analyzed.
Interpreting Output Results
The SpeciesNet output is provided in JSON format and includes the following information for each image:
- File path
- Name of the detected animal species
- Confidence score of the prediction
- Detection location information (bounding box coordinates)
- Basis for the prediction (classifier, detector, geographic information, etc.)
Output files can also be converted to the MegaDetector format for importing into tools such as Timelapse.
Practical Examples
Use in Academic Research
SpeciesNet is being utilized as an innovative tool in academic research. For example, in a pilot study in the tropical forests of the Congo Basin, autonomous drones equipped with SpeciesNet detected 78% more mammalian individuals compared to conventional methods.
Of particular note is that this technology has improved the efficiency of observing nocturnal creatures, leading to an update in biodiversity records from the previously confirmed 42 species to 89 species. This is a significant advancement leading to new academic discoveries.
Furthermore, new research approaches applying SpeciesNet technology are being developed, such as the "CoralGuardian" system, which estimates the health of coral reefs from the distribution patterns of symbiotic organisms.
Use in Conservation Activities
In the field of biodiversity conservation, SpeciesNet is also bringing about major changes. For instance, in anti-poaching efforts, a remarkable result has been reported: illegal hunting cases decreased by 67% year-on-year in protected areas where real-time image analysis systems were introduced.
In the tropical forests of Southeast Asia, SpeciesNet has been integrated into real-time monitoring systems aimed at poaching prevention and is being used for population management of endangered species. This has significantly improved the efficiency and effectiveness of conservation activities.
Use in Citizen Science Projects
In the "Global BioScan" project, SpeciesNet analyzes biological images taken by citizens with their smartphones to build a global biodiversity distribution database. To date, more than 4.7 million images have been collected from 132 countries, leading to the discovery of 17 new species.
Additionally, in the field of education, online ecology courses such as "BioAI Academy" have been established, providing practical curricula using SpeciesNet as a teaching material. The number of graduates in the 2025 academic year exceeded 12,000, and it is noteworthy that 34% of them are from developing countries.
Contribution to Sustainable Agriculture
In rice-growing regions of Southeast Asia, a pest identification system applying SpeciesNet has been introduced, successfully reducing pesticide use by 58% while maintaining yields. This technology is scheduled to be rolled out to 17 countries by 2026 through a support program by the FAO (Food and Agriculture Organization of the United Nations).
In this way, SpeciesNet contributes not only to direct wildlife conservation but also to the realization of sustainable agriculture. Establishing farming methods that consider biodiversity is also of great importance for long-term food security.
Future Outlook and Challenges
Directions for Technical Development
Several technical developments are expected in SpeciesNet's future development roadmap:
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Integration of 3D Shape Analysis Technology
By applying the Inflated 3D ConvNet technology, which has a proven track record in Google's medical field, it will become possible to analyze the three-dimensional structure of images taken from multiple angles. This is particularly expected to improve identification accuracy for camouflaged organisms. -
Introduction of Multimodal Learning
Development is underway for a multi-dimensional identification system that integrates bird song/call analysis technology with image recognition. Once realized, this will enable nocturnal observation and biological tracking in dense forest environments without relying solely on visual information. -
Expansion of Micro-organism Identification Functions
The "MicroNet" currently under development aims to enable morphological analysis at a resolution of 0.1μm, targeting improved accuracy in evaluating soil microbial diversity.
Current Technical Limitations
The main challenges for the current model are as follows:
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Identification Accuracy for Micro-organisms
Sufficient accuracy has not yet been achieved in the analysis of insects and microbial communities with a body length of less than 5mm. There is a need for the development of specialized extension models for macro photography. -
Dynamic Behavior Analysis Capability
Currently, SpeciesNet primarily focuses on the analysis of still images and cannot analyze animal behavior patterns in detail. Strengthening the analysis functions for time-series data remains a challenge. -
Computational Resource Requirements
The requirement for high-end GPUs acts as a barrier to adoption, particularly in resource-limited regions. Development of more lightweight models is also underway.
Ethical Challenges and Responses
With the spread of SpeciesNet, progress is also being made in addressing ethical challenges:
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Respect for Indigenous Knowledge
The "AI Conservation Ethics Council," established in October 2025, is developing guidelines for system implementation in indigenous territories, and dialogue with representatives from 43 indigenous communities is ongoing. -
Privacy Protection
With the development of the "BlurNet" algorithm, a feature has been implemented to automatically blur identifiable background elements in camera trap images while maintaining biological identification accuracy. -
AI Ethics Guidelines
The AI Ethics Guidelines (3rd Edition), released in June 2025, mandated the introduction of a framework to evaluate the impact of species misidentification on ecosystems.
Summary
Google's SpeciesNet is revolutionizing biodiversity conservation and wildlife research. Automated identification of trail camera images has realized ecosystem monitoring at a scale previously impossible, leading to a dramatic improvement in research efficiency.
To summarize the main features of SpeciesNet:
- High-precision species identification through a two-stage architecture
- Training based on a database of over 65 million images
- Ability to identify more than 2,000 types of organisms
- Processing capacity of 3.6 million images per hour
- Open-sourced under the Apache 2.0 license
Its use is expanding across various fields such as academic research, conservation activities, and citizen science, yielding concrete results like poaching prevention, new species discovery, and the realization of sustainable agriculture.
Looking ahead, further evolution is expected with the introduction of 3D shape analysis and multimodal learning. At the same time, addressing ethical challenges such as respect for indigenous knowledge and privacy protection is crucial.
The fusion of AI technology and biodiversity conservation is a prime example of building a sustainable future through the power of science and technology. For a world where rabbits, humans, and all living creatures on Earth can coexist, the role played by technologies like SpeciesNet will become increasingly important in the future.
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