---
lang: en
title: "Reva: no-code data labelling and AI model training"
url: https://nabrio.com/reva
canonical: https://nabrio.com/reva
updated: 2026-10-27
publisher: Nabrio Co., Ltd. (nabrio.com)
---

# Your local, private ML lab.

Label, train, export and manage datasets and models, all without code. Reva turns your own images into custom AI models, keeps the data on your own hardware, and deploys models to Nara in one step.

## From raw images to a production model, on your own machine

Off-the-shelf AI does not know your products, your parts or your defects. Reva closes that gap. Import or capture images, organise them into versioned datasets, label them with fast annotation tools or let AI pre-label them, balance the data with augmentation, and train detection and classification models with a few clicks. Compare experiments, check the metrics, and export the best model to Nara, where it starts working on the line with no configuration.

## What Reva gives each team

- **QA and process engineers**: Custom defect and part models without needing a data scientist.
- **Annotation teams**: Fast labelling tools, AI-assisted auto-annotation, and review roles.
- **Data and ML teams**: Versioned datasets, repeatable experiments, standard formats, and export to ONNX and PyTorch.
- **IT and compliance**: Training on-premise, with no images uploaded to third-party clouds.
- **Businesses**: Lower cost than outsourcing, and ownership of their data and their models.
- **Solution builders**: Custom models for the products they build on Nara, and their own training engines through MCP-based plugins.

## The whole model lifecycle, without code

- **Data and dataset management**: Projects and datasets in one place, with versions and train/validation/test subsets, import from standard formats with conflict handling, and modification history.
- **Annotation and labelling**: Bounding boxes, rotated boxes, polygons, keypoints, masks and image-level labels, with keyboard shortcuts for high-throughput labelling.
- **Auto-annotation**: Pre-label images with zero-shot foundation models from a text prompt, or with your own trained models, then review and correct. Augmentation reduces bias and multiplies training data.
- **Training and evaluation**: Experiments with configurable augmentation, hyperparameters and schedules, a training queue, live loss curves and metrics, early stopping, and side-by-side comparison. Automatic hyperparameter tuning on eligible tiers.
- **Results you can trust**: Check mAP and other metrics, logs and ETA as models train, and pick the best experiment.
- **Export and deployment**: Export as a Nabrio Model (NAM) for plug-and-play deployment in Nara, as ONNX for other runtimes, or as PyTorch for further development.

## What you can label and train today

| Task | Label | Auto-annotate | Train |
| --- | --- | --- | --- |
| Object detection | ✓ | ✓ | ✓ |
| Image classification (single label) | ✓ | ✓ | ✓ |
| Multi-label image classification | ✓ | ✓ | ✓ |
| Instance, semantic and panoptic segmentation | ✓ |  | Coming |
| Keypoint detection | ✓ |  | Coming |

Training plugins currently available: MMDet and RF-DETR for object detection, and image classification (single and multi-label). More are in development. Plugins use the open Model Context Protocol (MCP), so partners and customers can add their own training engines.

## Bring your existing datasets

| Format | Import | Export |
| --- | --- | --- |
| COCO / COCO (Reva) | ✓ | ✓ |
| Pascal VOC (standard and flat / LabelImg) | ✓ | ✓ |
| YOLO (Ultralytics, Darknet, Roboflow; flat / LabelImg) | ✓ | ✓ |
| Image classification folders | ✓ | ✓ |
| Image classification TXT / CSV | ✓ |  |

## Private and multi-user

Reva runs on your own workstation or server, so images and models stay on-premise. Six roles support team workflows: Super Admin, Admin, Trainer, Annotator, Annotation Supervisor and Monitor. Optional cloud backup is available on eligible tiers.

## Windows 11 workstation

Annotation works on an ordinary PC. Add an NVIDIA GPU when you are ready to train.

| Use | Minimum |
| --- | --- |
| Annotation only | 2 GHz quad-core CPU, 8 GB RAM, 500 MB plus dataset storage |
| Training | 2 GHz hexa-core CPU, 16 GB RAM, 10 GB storage, NVIDIA GPU (RTX 3060 / 8 GB VRAM or better recommended) |