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SpeciesNet is Google’s open-source image-classification tool for wildlife photos captured by motion-triggered camera traps. Used with MegaDetector, it can help conservation teams sort large image collections, but its labels come from a fixed set and its predictions are not a substitute for human review.
Google reports that SpeciesNet was trained on more than 65 million labeled images and can classify nearly 2,500 animal categories. Here is what it does, how to run it, and what its reported accuracy figures do—and do not—mean.
Why camera-trap projects use automated classification
Camera traps are motion-triggered cameras placed outdoors to record wildlife. Projects can accumulate large collections of images, and manually reviewing every frame can become a significant bottleneck.
SpeciesNet is intended to help wildlife researchers and conservation teams process those collections. Google places it within Google Earth AI, its collection of geospatial tools, datasets, and models, and describes its purpose as supporting wildlife research and conservation.
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Google says SpeciesNet had been operating within the Wildlife Insights platform since 2019 and was released as open-source software about a year before Google’s March 2026 retrospective. The repository provides setup and command-line instructions and describes how to obtain model weights.
How SpeciesNet works with MegaDetector
SpeciesNet is the classification part of a workflow that can combine it with MegaDetector. MegaDetector locates objects of interest, while SpeciesNet assigns labels to detected content. The classifier uses an EfficientNet V2 M architecture.
| Workflow part | Role |
|---|---|
| MegaDetector | Finds objects such as animals, people, and vehicles in images. |
| SpeciesNet | Classifies detected content using its fixed set of labels. |
| Optional workflow choices | Can use geographic information and can process multiple detections; the repository also documents video workflows. |
The label set includes individual species, broader taxonomic groups, and non-animal classes such as blank images and vehicles. Therefore, an output is not always a species-level identification.
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By default, run_model uses MegaDetector to locate animals and classifies the highest-confidence detection in each image. That can be limiting when an image contains multiple animals or when every detection matters. The repository documents options for processing multiple detections.
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Google Research reports the following results for a held-out test set of camera-trap projects:
| Reported result | What it means |
|---|---|
| 99.4% | Share of images containing animals in which the model found an animal. |
| 83% | Share of cases categorized to the species level. |
| 94.5% | Correctness among predictions that were categorized to species level. |
These are Google-reported results, not a guarantee for a particular project. In particular, 94.5% describes the correctness of species-level predictions; it does not mean that 94.5% of all images in every collection will receive a correct species label. Google reports more than 65 million training images and nearly 2,500 animal categories. The model’s fixed label set and the images in a project affect what it can identify.
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Google points readers to a 2024 study for fuller details about the training data, performance, and evaluation. The cited results do not establish uniform performance across regions, species, cameras, or image collections.
How to run SpeciesNet on camera-trap photos
The official repository describes a local command-line workflow, while Google also points users to Wildlife Insights for a hosted environment for managing images and collaboration. Choose between them based on setup effort, project and image management, collaboration needs, and whether the model’s geographic and taxonomic scope suits your work.
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| Access path | Best fit | Considerations |
|---|---|---|
| Local command line | Users who want to run the documented workflow on their own setup. | Requires following repository setup and command-line instructions and obtaining model weights. |
| Wildlife Insights | Teams seeking a hosted environment for images and collaboration. | Evaluate how its project and data-management capabilities fit your needs. |
Local command-line workflow
For current installation and invocation instructions, consult the official SpeciesNet repository. Its documentation covers setup, model weights, and command-line options. A basic starting point is to check the available options after installing the package:
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- Organize your camera-trap images and retain their associated metadata.
- Follow the repository’s current instructions to install SpeciesNet and obtain the model weights.
- Check the command-line options and run a small, representative batch first.
- Choose the documented workflow for the number of detections you need to process, and provide geographic information if appropriate.
- Keep the original images and review predictions, especially when identification matters for conservation or management decisions.
The repository says there is no official fine-tuning path, although it offers a do-it-yourself tutorial. Do not treat custom fine-tuning as an officially supported workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Examples of reported use
Google reports use by research groups in Colombia, Idaho, Australia, and Tanzania, with examples involving pumas, ocelots, elk, black bears, cassowaries, musky rat-kangaroos, lions, and elephants. Google also names wildlife and transportation agencies in the United States and Canada as users. These are Google-reported examples, not independent evidence that performance is uniform across deployments.
Google’s researchers describe SpeciesNet as a way to use conservation partners’ labeled images to classify nearly 2,500 animal categories in camera-trap images. These tools can reduce a manual-review bottleneck, but the available sources do not establish that every project can safely dispense with human review.
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Limitations to keep in mind
- It is specialized: SpeciesNet is documented for motion-triggered camera-trap images, not as a general-purpose wildlife image system.
- Its labels are fixed: The output may be a broader taxonomic or non-animal category rather than a species name.
- Default processing may focus on one detection: Use documented multiple-detection options when every animal in a frame matters.
- Reported performance has limits: Google’s test figures do not guarantee results for a specific region, species, camera, or project.
- Human review remains important: The sources describe model predictions and workflows, not a basis for removing people from consequential verification.
Can a trail camera be used with SpeciesNet?
A motion-triggered wildlife camera is a reasonable way to collect camera-trap images, but no particular camera model is required or endorsed for SpeciesNet. You can run the workflow on suitable images you already have; buying a camera is not a prerequisite for downloading or running the software.
FAQ
What is SpeciesNet?
SpeciesNet is Google’s open-source classifier for camera-trap images. It can be used with MegaDetector, which locates objects in the image before classification.
How accurate is SpeciesNet?
Google reports that it found animals in 99.4% of animal-containing images in a held-out test set of camera-trap projects, categorized 83% of cases to species level, and was correct in 94.5% of those species-level predictions. These reported results are not a guarantee for every project or image collection.
How do I run SpeciesNet on my camera-trap photos?
Use the setup and command-line instructions in the official repository to install the package, obtain model weights, and select the appropriate workflow. Google also points users to Wildlife Insights for a hosted image-management and collaboration environment.
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The default workflow classifies the highest-confidence detection in each image. The repository documents options for processing multiple detections, which are more appropriate when every animal in a frame matters.
Can SpeciesNet identify every animal species?
No. It assigns labels from a fixed set of nearly 2,500 animal categories, including broader taxonomic groups. Some images may not receive a species-level label, and the reported category count is not a promise that every species is represented.
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