An end-to-end computer vision MLOps platform — label datasets, train models, manage deployments, and close the feedback loop. One interface for the entire vision AI lifecycle.
End-to-End Computer Vision MLOps
Vivega Vision Platform bridges the gap between raw image data and production-grade vision AI. Annotate datasets with precision, auto-detect objects with YOLOv8, extract text with OCR, train custom models, and deploy them — all without switching between a patchwork of disconnected tools.
Every stage of your pipeline, in one platform.
Images or ZIP datasets
Canvas bbox + multi-class
YOLOv8 + OCR pre-fill
YOLO · COCO · VOC · OCR
Track, version, and serve
Vivega Vision Platform replaces the fragmented workflow of separate labeling tools, training scripts, model registries, and deployment configs. Teams upload raw image datasets, use the canvas-based labeling tool to draw bounding boxes and assign classes, then let the AI auto-suggest boxes and pre-fill OCR text. Annotated data exports in the exact format your training framework expects — YOLO, COCO, Pascal VOC, or PaddleOCR. From there, training runs are tracked, models versioned, and deployments managed through the same interface.
Draw bounding boxes on images with mouse or touch. Multi-class support — label a car, a number plate, and a person in the same image.
Run one-click auto-detection on any image. Pre-trained bounding box suggestions appear instantly — accept, adjust, or delete before saving.
Auto-read text inside any bounding box using EasyOCR. Perfect for licence plates, serial numbers, and product labels. Editable before saving.
Export in YOLO, COCO JSON, Pascal VOC XML, or PaddleOCR format — ready to feed directly into your training framework.
Monitor loss curves, mAP, and epoch progress in real time. Track experiments across datasets and model versions with MLflow integration.
Deploy trained models as inference endpoints. Version-control deployments and roll back with one click. Tracks requests, latency, and uptime.
Your annotations, in the exact format your training framework expects.
YOLOv5 / YOLOv8
Normalised cx cy bw bh per line — one .txt per image, images/ and labels/ dirs.
COCO JSON
Single annotations.json with images, categories, and bbox in pixel coordinates.
Pascal VOC XML
One XML file per image with <bndbox> coordinates. Supported by most classic frameworks.
PaddleOCR Format
Cropped plate images + Label.txt + rec_gt.txt — ready for PaddleOCR recognition training.
Computer vision teams building detection and recognition models
ALPR / traffic tech companies training custom plate recognition models
Smart manufacturing teams building defect detection pipelines
Retail and logistics building barcode, SKU, and product detection systems
Research labs needing a fast, structured annotation workflow
Keyboard shortcuts — ← → to navigate images, 1–9 to select class, Del to remove box
Auto-suggest confidence threshold — tune how aggressive auto-detection is
Multiple objects per image — car + number plate + person, all independently labelled
Built on FastAPI + Next.js — deploy on-premise or in your private cloud
PostgreSQL / SQLite storage, MinIO object storage, MLflow experiment tracking
Built With
Request access to the Vivega Vision Platform — available for on-premise deployment or as a managed private instance.