How much does it cost to outsource image annotation?
Pricing depends on task type (boxes vs polygons vs keypoints), attribute complexity, and QA requirements. Abaka offers real, transparent rates where applicable—for example, Image Editing is $8/hr and Dense Captioning is $6/hr, and automotive Road Lane labeling is $3/km. For image annotation programs, we typically scope a pilot first to confirm throughput, edge-case rate, and acceptance criteria, then lock a production plan with clear unit economics. Talk to an Expert and we’ll propose a costed plan tied to your spec.
How fast can you deliver an image annotation pilot and first production batch?
Most teams can complete a pilot in Week 1–2 after scoping, then scale into production in Week 2–3 once quality gates pass. Timing depends on how finalized your taxonomy is, how many edge cases exist, and whether you need tracking across video or complex attributes. Abaka accelerates ramp with pre-calibrated workflows, gold sets, and Abaka Forge automation, but we keep human review in the loop to prevent quality drift. You’ll get a delivery calendar and weekly reporting from day one.
What image annotation types and output formats do you support?
We support common image labeling needs including bounding boxes, polygons/instance masks, semantic segmentation, keypoints/pose, and attribute tagging. For delivery, we can export in widely used formats such as COCO JSON, YOLO TXT, Pascal VOC XML, PNG masks, and CSV/JSONL sidecars for attributes and metadata. If you have a custom schema, we can map outputs to your training pipeline as long as the rules are explicit and testable. Abaka Forge helps standardize exports across batches.
What accuracy can I expect from outsourced image annotation?
With calibrated guidelines, gold sets, and multi-layer QA, Abaka targets up to 99% accuracy depending on task complexity and definition clarity. Accuracy is not a single number—we measure it by label type (box overlap/IoU thresholds, boundary correctness for masks, attribute agreement, and class confusion) and by edge-case category. During the pilot, we quantify disagreement and failure modes, then adjust the spec and reviewer checks to raise consistency. You’ll receive quality reporting that’s tied to your acceptance criteria.
How do you handle security, NDAs, and compliance for image data?
Abaka operates with strict NDAs, segregated secure pipelines, and auditable access controls designed for enterprise and research programs. We support SOC 2 and ISO 27001 aligned operations and handle GDPR and CCPA requirements. Your data remains exclusively yours—never repurposed, resold, or shared—and we maintain full IP provenance (including 0% copyright risk on collected data). In practice, this means role-based permissions, controlled exports, and defined retention policies tailored to your program’s risk profile.
Can you annotate multilingual or region-specific imagery at scale?
Yes. Abaka supports global programs with annotators across 50+ countries, which helps when imagery includes region-specific signage, packaging, uniforms, or culturally specific objects. For tasks that require language knowledge (labels, OCR cues, or region-specific attributes), we can route work to appropriate teams and add calibration examples to reduce ambiguity. We also version taxonomies so regional differences don’t silently alter class meaning. Your team gets consistent outputs across geographies with measurable QA and reviewer adjudication for edge cases.
How is Abaka different from other image labeling companies?
Abaka combines enterprise-grade operations with a platform (Abaka Forge) that standardizes workflows, QA gates, and exports across modalities. We’re also structurally aligned with your interests: we never build models that compete with you, and your data is never repurposed or resold. Unlike vendors that optimize only for speed, Abaka caps per-annotator throughput (500 files/day) to reduce fatigue errors and uses multi-layer QA with gold sets and adjudication. You get predictable quality, not just volume.
What happens if we need to change the labeling guidelines mid-project?
Change requests are normal—new edge cases appear, taxonomies evolve, and model failures reveal missing attributes. Abaka manages changes through versioned specs and controlled rollout. We’ll quantify impact (what needs relabeling vs what can remain), update gold sets, recalibrate annotators and reviewers, and mark batches by spec version so training and evaluation remain comparable. Abaka Forge helps enforce the correct version per task, reducing the risk that mixed rules leak into the same dataset split.
Can we start with a small pilot before committing to a large annotation run?
Yes—most programs start with a pilot designed to validate guideline clarity, estimate edge-case rates, and confirm export compatibility with your training stack. A good pilot includes a representative sample across environments and difficult scenarios, plus measurable acceptance criteria (per class and per label type). After the pilot, you’ll get a clear production plan: throughput expectations, QA approach, and a delivery schedule. This reduces risk and prevents scaling a spec that’s not yet stable.
Who owns the annotated data and can Abaka reuse it?
You own your data and the resulting annotations. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared—and we never build models that compete with you. We also maintain full IP provenance, which helps ensure the dataset remains defensible for audits and downstream commercial use. If you require additional contractual language around ownership, retention, or deletion, we can align terms during scoping and implement the controls operationally in our secure pipeline.
What tools do you use for image annotation and QA management?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production workflows across image, video, and 3D/4D point cloud. Abaka Forge supports task configuration, role-based access, QA sampling and gating, adjudication, and export standardization. It also applies large-model automation for suitable steps to accelerate delivery while keeping humans in the loop for final decisions. If your team has custom validators or schema checks, we can integrate them into the delivery process.
What is the minimum dataset size for outsourcing image annotation to Abaka?
There’s no fixed minimum, but the best results come when you have enough volume to justify a stable spec, calibration, and QA measurement—often a few thousand images for a meaningful pilot. For smaller datasets, we can still help by focusing on high-precision labeling and expert adjudication, especially if edge cases are critical. If you’re early-stage, we can design a phased plan that starts small, proves quality, and then scales as your training needs grow, without changing vendors or tools.