Cvat Pull

Pull CVAT Annotations

Pull human-corrected annotations from CVAT into your local dataset.

Pre-Flight Checklist

• [ ] yolo-project.yaml has cvat: section with url and project_id
• [ ] CVAT_ACCESS_TOKEN env var is set
• [ ] CVAT instance is reachable (default: http://localhost:8080)

Workflow

1. Identify What to Pull

• Ask the user which CVAT task or project to pull
• Or read the default project_id from yolo-project.yaml

2. Pull Annotations

yolo-cvat pull --task 
# or
yolo-cvat pull --project 

3. Validate the Downloaded Dataset

yolo-validate 

Check for:

• Valid data.yaml with correct class names
• Image/label count matches expectations
• No annotation errors

4. Compare with Existing Dataset

If the user already has a local dataset:

• Compare class distributions
• Check for new images vs corrections
• Suggest merge strategy if combining

5. Merge if Needed

yolo-merge --sources   --output 

Decision Tree

Has existing local dataset?
├── Yes → Compare distributions → Suggest yolo-merge
└── No → Set as primary dataset

Validation warnings found?
├── Yes → Flag issues, ask before training
└── No → Ready to train

Class distribution changed?
├── Significantly → Warn user, may affect model balance
└── Minor → Proceed normally

Guardrails

• NEVER overwrite an existing dataset directory without user confirmation
• ALWAYS run yolo-validate after pulling
• Report class distribution changes clearly
• If merge is needed, show the user what will change before executing

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: MacroMan5
• Source: MacroMan5/autotrain-yolo
• License: MIT

Install and usage instructions live in the source repository linked above.