How much do Image Annotation Services cost?
Pricing depends on annotation type (boxes vs masks vs dense captioning), QA depth, and whether we use per-hour or per-unit structures. As reference points: Dense Captioning is priced at $6/hr, Image Editing at $8/hr, and STEM Generalist work at $12/hr; for lane-style spatial labeling, Road Lane work is $3/km. We’ll scope your taxonomy, acceptance criteria, and output formats, then propose a blended rate card and a pilot batch budget so you can validate quality before scaling.
How fast can you deliver an image annotation project?
Most teams start with a pilot and move into production within a predictable 2–3 week cycle once specs are locked. Day 0–3 is typically scoping, taxonomy definitions, and acceptance criteria. Week 1–2 covers a pilot batch plus adjudication and guideline calibration. Week 2–3 is the production ramp with QA reporting and versioned deliveries. Exact timing depends on complexity (e.g., instance masks vs boxes) and volume, but we design the plan around your training milestones.
What annotation types and output formats do you support for Image Annotation Services?
We support 2D bounding boxes, polygons, semantic segmentation, instance segmentation, keypoints/pose, and attribute tagging (scene metadata, object state, difficulty flags). Output formats commonly include COCO JSON (detection, masks, keypoints), VOC XML, PNG mask stacks, CSV/TSV attribute tables, and custom JSON schemas that align with your training code. We also provide QA metadata and versioning so downstream pipelines can track exactly which guideline set produced each label batch.
What accuracy can I expect from your image annotation team?
We target 99% accuracy using multi-layer QA and adjudication, with explicit acceptance criteria defined during scoping. Accuracy is managed through measurable checks: sampling plans, error taxonomies, reviewer calibration, and controlled throughput (up to 500 files/day per annotator) to reduce fatigue-driven mistakes. For difficult classes, we escalate to scholar-network reviewers and run adjudication to resolve disagreements. You’ll see quality reporting per batch so you can monitor drift as volume scales.
How do you keep my images and IP secure during annotation?
Abaka uses strict NDAs, segregated secure pipelines, and enterprise-aligned compliance practices including SOC 2 and ISO 27001, plus GDPR and CCPA support. Access is controlled and auditable, and deliveries can be constrained to the formats and channels your security team approves. Importantly, we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. For collected data, we maintain full IP provenance with 0% copyright risk targets.
Do you support multilingual labeling instructions and global datasets?
Yes. Abaka operates across 50+ countries and can support multilingual instructions, multilingual attribute vocabularies, and region-specific edge cases. For global datasets, we help you standardize taxonomy definitions while still capturing local variation through controlled attributes. Abaka Forge supports templated guidelines and validation rules to keep labels consistent even when multiple languages are involved. If needed, we can also add text-side annotations (captions, OCR corrections, or image-text pair validation) to support multimodal training.
How are you different from other image labeling vendors?
Many vendors optimize for raw throughput; Abaka optimizes for repeatable, auditable quality that holds up over multiple dataset versions. We combine a large, specialized workforce with throughput caps (500 files/day per annotator) and multi-layer QA to protect consistency. Abaka Forge provides versioning, validation rules, and audit trails so your team can trace results back to guideline changes. We also differentiate on trust: we never build competing models, and your data is never repurposed or resold.
Can we change the labeling taxonomy or guidelines mid-project?
Yes—and we plan for it. Taxonomy changes are handled through versioned guidelines so you can roll updates forward without silently mixing definitions. We’ll document the change, update Abaka Forge templates and validation rules, and run a small calibration batch to confirm the new rules match your intent. If you need backfills (re-labeling older data under the new schema), we’ll scope the delta and propose the most cost-effective approach, such as focusing only on impacted classes or uncertain samples.
Can you run a pilot before committing to full production?
A pilot is the recommended starting point. We typically run a small batch to validate taxonomy, boundary rules, and acceptance criteria, then review results with your ML leads and stakeholders. The pilot includes QA reporting and adjudication on ambiguous samples so the “definition of done” is explicit. Once approved, we scale production in controlled batches with the same guidelines and tooling. This structure reduces rework and shortens the path to reliable training data.
Who owns the labeled data and derived annotations?
You do. Abaka’s positioning is clear: your data is exclusively yours—never repurposed, resold, or shared. We operate under strict NDAs and segregated pipelines, and we deliver outputs in the formats you specify along with audit metadata. If Abaka collects data on your behalf, we maintain full IP provenance to support 0% copyright risk targets. Contract details can match your organization’s procurement requirements, including data retention and deletion policies.
What tools do you use for Image Annotation Services?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production across image, video, text, RLHF, and 3D/4D. For image projects, Forge supports templated instructions, validation rules, reviewer queues, adjudication workflows, and dataset versioning. Large-model automation can speed up suitable tasks (up to 50x faster) while maintaining human verification for correctness. Platform usage can be credit-based at $0.20 USD per credit, depending on workflow design.
Is there a minimum project size for Image Annotation Services?
We support both pilots and large production programs. There’s no strict minimum, but the most effective engagements start with enough volume to validate edge cases—often a pilot batch that includes representative scenes, difficult examples, and failure modes you care about. If your dataset is small, we focus on maximizing signal: stronger acceptance criteria, deeper QA, and targeted sampling. If you’re scaling up, we design a cadence (often 2–3 weeks) with consistent deliveries and change control.