High-quality data annotation on CVAT

Can you share a sample of your labeling guidelines or a small test task to better understand your accuracy expectations?

1. Location: Autonomous Pre Annotation Core Platform Section
Issue: The claim that zero shot models like SAM GroundingDINO and YOLOv10 can reliably deliver autonomous pre annotation at scale with 97.2 percent precision across general datasets appears optimistic without dataset specific benchmarking. In real CVAT style pipelines zero shot outputs still require substantial human correction depending on domain shift.
Recommendation: Clarify that precision metrics are conditional on dataset type and include benchmark conditions such as domain dataset size and annotation class complexity. Adding error rate ranges per modality would improve credibility.

2. Location: Performance Metrics Banner Top Section 10,000 plus images per day sub 200ms latency
Issue: The combination of 10,000 plus images per day throughput with sub 200ms inference latency per image lacks operational context such as hardware specifications batch processing assumptions queueing overhead and human in loop time. This can be interpreted as inference only performance rather than end to end annotation throughput.
Recommendation: Separate model inference latency from full pipeline throughput from ingestion to inference to QA to export. Include infrastructure assumptions such as GPU type concurrency level and batch size.

3. Location: Competitive Positioning Table
Issue: CVAT is presented as limited in active learning and slower inference which creates a category mismatch since CVAT is primarily an annotation interface rather than an AI inference system. This may lead to misleading benchmarking comparisons.
Recommendation: Reframe comparison axes clearly by separating labeling tools from AI assisted annotation systems to ensure fair benchmarking against comparable platforms.

4. Location: Confidence Pipeline Section greater than 95 percent auto approval system
Issue: The auto approval mechanism for greater than 95 percent confidence labels assumes strong calibration that may not hold under domain shift or rare class distributions. Confidence scores in vision models are often not well calibrated without additional validation.
Recommendation: Include calibration methodology such as temperature scaling reliability testing or periodic human audit sampling to validate confidence thresholds.

5. Location: Pricing Section 0.50 to 2.00 per image versus competitors
Issue: The pricing comparison does not clearly distinguish between raw annotation cost and fully validated labeled data cost including QA rework and edge case handling. This can create ambiguity in real cost effectiveness.
Recommendation: Break down pricing into components such as AI pre labeling human review QA cycles and final approved dataset cost for clearer enterprise understanding.

Can you share a sample of your labeling guidelines or a small test task to better understand your accuracy expectations?
What is the estimated volume of images/videos and expected daily or weekly workload?
Which annotation types will be most frequent (bounding boxes, segmentation, keypoints, or tracking)?
What level of quality control or review process do you have in place?
What is your expected turnaround time for tasks or batches?
Are there specific classes or edge cases that require special attention?
Will the work be done entirely within CVAT, or are there additional tools involved?
How is performance measured (accuracy %, consistency, speed)?
What is the budget structure for the test phase and ongoing work?
When are you looking to start the initial test task?