About
Zamba Cloud brings state-of-the-art computer vision to wildlife conservation, removing the technical barriers that have traditionally made processing large volumes of camera trap footage slow and expensive. Researchers and conservationists can upload media files directly or point the platform at an FTP server, then let Zamba's pretrained models run automatic species classification in the background. When processing finishes, users receive an email and can download a labeled spreadsheet mapping each file to the most likely species present — instantly filtering out blank triggers and surfacing footage of interest. The platform offers multiple official models optimized for distinct geographies and use cases: blank-vs-non-blank detection, African species models for jungle ecologies (including a video-native SlowFast variant for better small-species detection), a European species model for non-jungle habitats, and a global image model covering 178 taxonomic classes. Beyond pre-built inference, Zamba Cloud supports custom model training — users supply labeled examples and the platform fine-tunes a specialized model that can even learn to recognize entirely new species or ecologies. Designed for field biologists, conservation NGOs, and academic researchers, Zamba Cloud requires no coding experience. It is the cloud-hosted counterpart to the open-source `zamba` Python package, making powerful wildlife AI accessible to teams who lack engineering resources.
Key Features
- Automated Species Classification: Upload camera trap videos or images and receive a labeled spreadsheet identifying the most likely species present in each file, eliminating manual review of blank triggers.
- Multiple Pretrained Geography Models: Choose from models optimized for Central, East, and West Africa (including a SlowFast video-native variant), Western Europe, or a global image model covering 178 taxonomic classes.
- Custom Model Training: Supply your own labeled media and Zamba Cloud fine-tunes a specialized model on top of the base model, enabling detection of new species or ecologies specific to your habitat.
- No-Code Interface: The entire workflow — uploading media, running inference, and training models — is accessible through a web interface with zero programming required.
- FTP & Direct Upload Support: Ingest large datasets either by uploading files directly to the platform or by pointing Zamba Cloud at an existing FTP server where field data is stored.
Use Cases
- Wildlife biologists processing thousands of camera trap images from African savanna surveys to identify and count species without manual review.
- Conservation NGOs training custom models on locally labeled footage to improve detection accuracy for rare or regionally specific species.
- Academic researchers filtering large volumes of blank-trigger camera trap videos to quickly isolate footage containing animals of interest.
- Field teams in Western Europe using the European species model to automatically classify non-jungle wildlife from remote camera networks.
- Conservation projects with no in-house data science capacity leveraging no-code model training to build specialized classifiers for new habitats.
Pros
- Truly No-Code: Conservation researchers without programming backgrounds can run AI-powered species classification and model training entirely through the web UI.
- Broad Model Coverage: Multiple pretrained models across different geographies and media types (video and images) mean users can find an appropriate starting point for most field contexts.
- Custom Model Training Included: The ability to fine-tune models on labeled local data dramatically improves accuracy for specific habitats and species not well represented in base training data.
- Asynchronous Processing with Notifications: Jobs run in the background and trigger email alerts on completion, so researchers are not blocked waiting for large batches to finish.
Cons
- Limited to Camera Trap Use Cases: Zamba Cloud is purpose-built for wildlife camera trap footage and is not a general-purpose computer vision or image classification tool.
- Geographic Model Gaps: Strong model support exists for Africa and Western Europe, but coverage for other regions (Asia, Americas) is limited to the broader global image model.
- Beta Status: The platform is still in beta, meaning features and documentation (particularly for the newer image support) are still actively evolving and may change.
Frequently Asked Questions
What types of media does Zamba Cloud support?
Zamba Cloud supports both camera trap videos and images. Image support was recently added, and the same workflow used for videos applies to images as well.
How many species can Zamba Cloud identify?
The official global base model can identify 178 taxonomic classes. Regional models such as the African species model are optimized for the species commonly found in Central, East, and West Africa.
Do I need to know how to code to use Zamba Cloud?
No. Zamba Cloud is designed specifically for researchers without programming experience. All classification and model-training tasks are performed through a point-and-click web interface.
Can I train a model to identify species not in the base model?
Yes. By uploading labeled media, you can fine-tune a custom model that builds on the base model's knowledge and can learn to predict entirely new species and ecologies.
How do I get my results after uploading media?
After uploading, Zamba Cloud processes your media in the background and sends you an email notification when it's done. You then log back in and download a spreadsheet with per-file species labels.
