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Object Count Review Studio

Detect supported common object classes, remove false positives, add missed-item markers and export reviewed evidence.

Free browser toolNetwork-assistedNo sign-up

Visual operations workspace

Object Count Review Studio

Detect supported object classes, remove false positives and add numbered corrections before export.

Annotated review canvas

Choose an image or load the review example.

Every result is an estimate until a person reviews the full image. Check image edges, reflections, occlusion, stacked items and partially visible objects before operational use.

About the Object Count Review Studio

Detect supported common object classes, remove false positives, add missed-item markers and export reviewed evidence.

Automatic detection is an optional first pass, not a final count. TensorFlow.js and COCO-SSD load from jsDelivr only when detection starts, then inference runs in the browser. The model recognizes a limited set of common classes and can miss, duplicate or misclassify visible items.

The review canvas separates model detections from human correction markers. Remove any false-positive detection, click the centre of missed items and use class totals where automatic classification is available.

Export the annotated PNG as visual evidence plus CSV or JSON audit data. Every operational count still requires review of image edges, occlusion, reflections, overlapping items and partially visible stock.

Key features

  • Opt-in local COCO-SSD detection
  • Detection confidence and class labels
  • Remove individual false positives
  • Numbered missed-item correction markers
  • Detected, correction and reviewed totals
  • Detected-class summary
  • Deterministic review example
  • Annotated PNG evidence
  • CSV and JSON audit exports
  • Private browser inference after model download

How to use

  1. 1Upload a sharp image with visible separation or load the review example.
  2. 2Optionally run supported-class detection; the model files download only when requested.
  3. 3Inspect every numbered box and remove false-positive detections.
  4. 4Click the centre of each missed item to add a correction marker.
  5. 5Compare the class summary with the visible scene.
  6. 6Export PNG, CSV and JSON evidence only after human review.

Examples

Review a object count review studio example
Object Count Review Studio deterministic example with numbered count evidence and review controls
Input: Twelve visible books and boxes
Output: Reviewed total, annotated PNG and CSV or JSON audit

Click Load review example to reproduce this state without downloading the detector. Replace it with a real operational image and verify every visible item.

Frequently asked questions

Is the reviewed count guaranteed?
No. A human must still inspect occluded, overlapping, reflected and partially visible items before operational use.
Which objects can the detector recognize?
COCO-SSD recognizes a limited set of common classes. It is not a universal warehouse, retail or specialist inventory model.
Can false detections be removed?
Yes. Every automatic detection appears in a review list with a remove action. Missed items can be added as manual numbered markers.
Does the image leave the browser?
The image remains local. TensorFlow.js, COCO-SSD and model files load from jsDelivr only when automatic detection is requested.
Which evidence exports are available?
Download an annotated PNG plus CSV and JSON audit records containing the reviewed count evidence.

Continue your workflow

Open a related tool to prepare your files or refine the finished result.