Templates for Computer Vision

Live training module

Plug live training into your labeling pipeline: images reach labelers in batches, and once the model is good enough, each batch arrives pre-labeled.

Prelabel batch
Live training
Quality gate
Move to labeling
Prelabel efficiency
+1

Labeling
Prelabel
Train
Image
Atomic
Grounding Dino

Live training: quick start

Label images while a model learns from every submit and draws boxes on the images still waiting, so your team moves from drawing to correcting

Skipped
Labeled
Labeling pool
Prelabeling
Labeling
+2

Labeling
Label
Train
Image
Atomic
Grounding Dino
Prelabel

Basic labeling pipeline

A labeling loop for your team: images cycle through labeling and review until accepted, so every image in your final dataset has passed review.

Review
Rejected
Return to labeling
Skipped
Accepted
+3

Labeling
Label
Review
Image
Atomic

Labeling with expert

Route difficult annotations to a specialist while routine work goes straight to final review; anything the reviewer rejects returns to the labeling pool for another pass.

Review pool
Rejected
Return to labeling
Labeling, Expert
Labeling pool
+4

Labeling
Label
Review
Image
Atomic

Active learning with live training

Label a dataset in small batches: a random first batch, then each round the images most similar to the model's validation failures, pre-labeled by a live training session. Evaluate on the test set when you stop.

Live training
Prelabeling with live training, Pre-label and accept
Stop after 10 rounds
Learning curve
Evaluate on test, Evaluate on validation
+5

Active Learning
Labeling
Object Detection

Labeling with auto quality check

Label a pool of images and let rule-based checks screen every submission before a reviewer sees it; failures return for another pass, and the completed set comes out with a quality report.

Labeling, Review
Auto quality check, Passed QC, Data Quality Control
Labeled
Unlabeled
Skipped
+4

Labeling
Label
Review
Image
Atomic

Filter-based data cleanup

Profile a dataset, then fix what the findings point at with rules — remove the images that should carry labels but don't, rename a misspelled class — and see the cleaned copy re-profiled next to the original. No labeling, no training, no model.

Dataset, Filtered Dataset
Filter images
Transform Annotations
Data Quality Control

Data Quality
Evaluate
Clean
Image
Atomic
Pcb Component Detection

Dataset intelligence

Run statistical, embedding and prelabeling checks on an imported detection dataset and merge them into one report: findings ranked by severity, and recommended next steps.

Data Quality
Evaluate
Image

Dataset quality control

Point a dataset at a quality-control step and get a report of what is inside — asset and annotation statistics. No labeling, no training, no model.

Dataset
Data Quality Control

Data Quality
Evaluate
Image
Atomic

Supervisely queue labeling

Keep the team labeling in Supervisely and let the queue run itself: annotators pull the next image from one shared pool instead of waiting on a batch, so nobody sits idle, and a reviewer sends a reject straight back to its author. Stage views show where every image stands right now.

Review
Rework, Waiting for labeling, Waiting for review, Accepted
Labeling
Labeling queue manager
Source dataset

Labeling
Label
Review
Image
Atomic

Detector evaluation

Score one detector against a labeled reference set — mAP/AP50/AP75/F1 with per-class breakdown and diagnostic examples, the acceptance check before shipping a model.

Predict EdgeCrafter
Evaluate object detection
Dataset

Object Detection
Evaluate
Image
Atomic
Edgecrafter

Run EdgeCrafter Detector on Images

See what a ready-made detector finds on your own images, before you spend anything on labels or training.

Predict EdgeCrafter
Dataset

Object Detection
Inference
Image
Atomic
Edgecrafter

Detector comparison

Score several ready-made detectors on the same labeled image set with the same eval code and find the best one — per-class mAP/AP50/AP75/F1, no training required.

Predict EdgeCrafter, Predict DEIM
Dataset
Compare object detectors
Evaluate object detection

Object Detection
Compare
Image
Atomic
Yolo
Edgecrafter
Deim

Train EdgeCrafter Detector on Images

Fine-tune an object detector on your own labeled images and see whether the training actually converged.

Dataset
Train/Val split
Train EdgeCrafter

Object Detection
Train
Image
Atomic
Edgecrafter

Labeling jobs in Supervisely

Hand out the images in a dataset as named batches — one Supervisely labeling job per annotator, one reviewer per job — and watch every image move through its stages without leaving OvalBee. An annotator labels their whole batch before submitting it; rejected images are reissued as a fresh job, and accepted ones collect in a final dataset. Runs on a schedule, so images added later join the same pipeline on their own. No model, no metrics.

Labeling
Rejected, Waiting for review, Accepted, Waiting for labeling
Review
Source dataset
Labeling jobs manager

Labeling
Label
Review
Image
Atomic

Import from Supervisely

Copy images and their annotations from a Supervisely project, dataset or collection into an OvalBee dataset that any other template can start from.

Import images
Dataset

Data Import
Import
Image
Atomic

Import from Dataset Ninja

Bring a public dataset from Dataset Ninja into OvalBee by its link, images and annotations included, ready for any other template to start from.

Import from Dataset Ninja
Dataset

Data Import
Import
Image
Atomic

Import images by drag & drop

Drop images from your computer onto the graph and get an OvalBee dataset that any other template can start from. Every drop adds a new version of the dataset.

Import drag & drop
Dataset

Data Import
Import
Image
Atomic