Guides
Understand the workflow, not just the buttons
Practical explanations, measured comparisons and real experiments for building computer-vision datasets with local, human-guided tools.
What is image annotation?
Image annotation labels images so a computer-vision model can learn from them. The types, how to choose one, the workflow, dataset formats and quality checks.
Read guide →Interactive Segmentation Is Zero-Shot. It Still Isn’t Auto-Labeling.
Interactive segmentation is genuinely zero-shot — but not autonomous auto-labeling. How SAM, CLIP/SigLIP 2, Grounding DINO and visual prompts split the work.
Read guide →How AI Is Changing Data Annotation: From Manual Labeling to Human-Guided Automation
AI is not auto-labeling your dataset for you. It is human-guided automation: search, zero-shot suggestions and one-click segmentation do the grind, you decide.
Read guide →Can you pre-label a computer vision dataset locally without sending a single image to the cloud?
A practical test of four EdgeCrafter ONNX models for private, local object-detection and instance-segmentation pre-labeling in the browser.
Read guide →MobileSAM vs SAM 2.1 vs SAM 3: Which One Is Actually Faster for Annotation?
MobileSAM, SAM 2.1 and SAM 3 Tracker benchmarked in AnnotateIt’s local browser runtime: encoder latency, click response, memory and what the results do not prove.
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