Annotation, quality & human feedback
Work in annotation tools (Label Studio / CVAT / Labelbox)
Set up projects, hotkeys, bounding boxes/segmentation/NER, export formats.
~6 focused hours·beginner
Tools: Label Studio, CVAT, Labelbox, hotkeys, export formats (COCO/YOLO/JSON)
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Set up a new labeling project with the right configuration (bounding box, NER, classification, segmentation)
- Use keyboard hotkeys to hit realistic throughput targets, not just mouse clicks
- Export labeled data in the format a downstream model actually needs (COCO/YOLO/CoNLL/JSON)
- Configure multi-annotator review/consensus workflows inside the tool
- Import pre-annotations or model predictions to speed up human review
- Troubleshoot common tool issues (export mismatches, project config errors)
- Move between tools quickly since different clients standardize on different platforms
Needs first: Label data accurately against guidelines
Learn — free, link-checked
The few resources that matter
Read · beginner · 25 min · docs.labelbox.com
Platform overview
Covers project setup and export formats in Labelbox, the third major annotation platform named in Indian AI job listings. — Labelbox
Read · beginner · 30 min · labelstud.io
Get started with Label Studio
Official quickstart for installing Label Studio and configuring your first labeling project with hotkeys. — HumanSignal / Label Studio
Read · beginner · 30 min · docs.cvat.ai
Getting started
Official guide to bounding boxes, segmentation, and export formats in CVAT, the most common open-source CV labeling tool named in job posts. — CVAT.ai
Read · intermediate · 25 min · labelstud.io
Set up task agreement
Shows how to configure and read inter-annotator agreement directly inside the tool you'll be evaluated in. — HumanSignal / Label Studio
Practice
Multi-annotator image labeling project in Label Studio or CVAT
Set up a Label Studio (or CVAT) project to bounding-box-label 100 images (e.g. Indian street/traffic scenes) for 3 object classes, label them yourself using hotkeys, export in COCO format, and document the project configuration and export mapping.
Done when
- Project is configured with the correct label schema for 3 classes and keyboard shortcuts are used (documented in README)
- All 100 images are labeled and exported in valid COCO JSON that loads without error
- README documents the project config (label config XML/JSON) so someone else could reproduce the setup
- At least one screenshot/recording of the labeling interface with hotkeys in use
Prove it
Evidence a recruiter can check
- Public repo with the exported COCO JSON, project config, and README
- A short screen recording showing hotkey-driven labeling speed
- A validation script that loads the exported file and confirms it's well-formed
Interview
Questions you'll get asked
- Walk me through setting up a new bounding-box project in CVAT or Label Studio from scratch.
- How do you configure a project so two annotators label the same items for agreement checking?
- What export format would you use for a YOLO object-detection model, and why does the format matter?
- How would you speed up labeling for a project with 50,000 images using pre-annotations?
- What hotkeys or shortcuts do you rely on to hit a daily throughput target?
- Describe a time a tool's export didn't match what the ML team needed — how did you fix it?