About
Zamba Cloud is a browser-based wildlife AI platform developed by DrivenData, designed to help conservation researchers process large volumes of camera trap videos and images without writing any code. Powered by state-of-the-art computer vision models, Zamba Cloud can automatically identify 178+ taxonomic species classes across Central Africa, East Africa, West Africa, Western Europe, and global habitats. Users can upload media files directly or connect an FTP server to batch-process footage. Once submitted, Zamba processes the files in the background and emails a notification when complete. A structured spreadsheet is then available for download, listing the most likely species detected in each file — making it easy to filter blank triggers and focus on meaningful footage. For researchers working in specialized or novel ecologies, Zamba Cloud supports custom model training. Upload a set of labeled videos or images and Zamba will fine-tune a base model using transfer learning, enabling it to recognize new species and environments that were not in the original training set. Multiple pretrained model architectures are available, including an image-based African species model, a video-native SlowFast variant optimized for small species detection, a European species model, and a global image model. The underlying zamba Python package is open source, and all official models are publicly accessible. Zamba Cloud is ideal for field biologists, conservation NGOs, and wildlife monitoring programs that need scalable, automated species identification without data science expertise.
Key Features
- Automated Species Classification: Detects and identifies 178+ taxonomic animal classes in camera trap videos and images using pretrained deep learning models.
- Multiple Pretrained Models: Choose from specialized models for Central/East/West Africa, Western Europe, and global image classification — including a SlowFast video-native architecture for small species detection.
- Custom Model Training: Upload your own labeled footage to fine-tune a base model, enabling detection of new species and ecologies unique to your field site.
- No-Code Interface: Process hundreds of videos or images through a point-and-click web UI with direct uploads or FTP server integration — no programming required.
- Spreadsheet Output with Email Notification: Receive a structured label spreadsheet for every media file analyzed, delivered via email notification when processing is complete.
Use Cases
- Wildlife conservation organizations processing thousands of camera trap images to identify and track endangered species populations.
- Field biologists filtering blank or false-trigger footage from camera traps to focus only on recordings with animal activity.
- Researchers training custom species detection models for unique regional habitats not covered by the default African or European models.
- Academic institutions running non-invasive biodiversity surveys using automated AI classification to reduce manual review labor.
- Conservation NGOs generating structured species occurrence data from remote camera networks for reporting and ecological analysis.
Pros
- No Coding Required: Researchers without data science backgrounds can run sophisticated ML classification workflows entirely through the web interface.
- Wide Geographic Coverage: Pretrained models span African jungles, European non-jungle ecologies, and a global image model, making it versatile for diverse field projects.
- Custom Model Support: Ability to train personalized models on new species and habitats using transfer learning from strong pretrained baselines.
- Free and Open Access: Zamba Cloud is free to use, and the underlying models and Python package are publicly available for transparency and reproducibility.
Cons
- Limited to Camera Trap Use Cases: The platform is purpose-built for wildlife camera trap data and is not suitable for general-purpose video or image classification tasks.
- Image Documentation Still Incomplete: Image support is newly added and documentation is still in progress, which may cause confusion for users exploring image-based workflows.
- No Real-Time Processing: Analysis is asynchronous — results are delivered by email rather than in real time, which may slow down time-sensitive fieldwork decisions.
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 works similarly to the existing video classification workflow.
How many species can Zamba Cloud identify?
The official base models can identify 178 taxonomic classes. Custom-trained models can be extended to recognize additional species specific to your field site.
Do I need to know how to code to use Zamba Cloud?
No. Zamba Cloud is a fully no-code web platform. You can upload media, run classification, and download results entirely through the browser interface.
How do I upload large volumes of camera trap footage?
You can upload files directly through the web interface or connect Zamba Cloud to an FTP server where your files are already stored, making batch processing of large datasets practical.
Can Zamba Cloud learn to identify species not in the default models?
Yes. By uploading a set of labeled videos or images, you can train a custom model that builds on the base model's knowledge and learns to predict entirely new species and ecologies.
