Image Annotation Outsourcing Services
On-demand, High-quality Image Annotation Services at Scale
Obtain training data for your computer vision ML models at scale. BUNCH allocates teams of image annotators and labelers to train your image datasets into structured training data.
Our in-house labelers are trained and our project management team is versed in industry-specific techniques particular to different types of imagery, such as self-driving vehicles, Medical AI, Precision Agriculture, Geospatial Intelligence, Agriculture, and many others.
Label any size from small datasets to long-term image annotation data packages with BUNCH. We allocate specialized annotation teams, ensuring the most reliable output quality using rigorous QA processes and utilizing double-pass annotation methods.
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Edge Cases — Not All Data was Born Equal
We've made ourselves experts in edge cases in most Computer Vision verticals dominating the AI space. Beyond the most common data patterns, the edge cases' data value defines the human contribution to the model through cultural insights. It determines the robustness of the input dataset and its parent ML Model. However, labelers being able to discern and assess edge cases on the spot with minimal bias is only achievable by a deep understanding of the guidelines, extensive training and exposure to the specific type of imagery, understanding the industry and the future practical use of the ML model, and holding a detailed library of "decision playbooks" in hand.
Outsourcing image annotation to experts allows you to feed rich, structured datasets to your various ML models, incorporating high-value human insights that enhance your AI systems' overall accuracy and performance.
Experts in the loop
For instance, a deep knowledge of the extensive array of categories and subcategories of objects, vehicles, and obstacles possible in a given real-world scenario allows an annotator to discern between signal and noise, categorize the objects and their parts under the suitable classes, using techniques like image segmentation or polygon annotation, and help structuring data the ML model of self-driving vehicles programs, drones, navigation systems or security and surveillance ML programs.
Similarly, labelers with foundations in biology will add deeper value to the annotated microscopy images, plant anatomy, and planting patterns on aerial imagery, identifying species and varieties of plants, crops, pests, and diseases for agricultural ML programs using bounding boxes or semantic segmentation.
In renewable energy, identifying anomalies, defects, and occlusions in the labeling (using keypoint, pixel segmentation, or polygon annotation) of aerial ortho-mosaic imagery of photovoltaic (PV) farms, including satellite or drone imagery, involves a deep training on the structural composition of the farms including types of strings, modules, cells, diodes and their average damages caused by environment, weather, and decay, that feeds reliable training data to predictive maintenance ML models.
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Power your AI algorithm with High Quality and accurate Data
Avoid data inconsistencies in your training data. Obtain high-quality datasets to train a robust and reliable ML model. We achieve the highest pixel-perfect image tagging using different strategies proven to have a huge impact on data quality while maintaining optimal efficiency.
Phase 1: Calibration Test
Before starting production, we run an annotation test in a sample representative of the entire dataset against preliminary annotation guidelines (draft labeling guidelines provided by the client). This allows us to determine an estimated AHT (Average Handle Time) and familiarize ourselves with the specific images and their complexity. We also anticipate ambiguities and edge cases that will later be useful during the production phase of our image annotation services.
Phase 2: Iterated Annotation Guidelines
We work closely with our clients to translate their requirements into a bullet-proof Annotation Guidelines document that compiles the class definitions, a library of identifiable objects, bodies and subcategories, edge cases, positive and negative examples, and escalation triggers. We iteratively improve this guide using feedback loops as we learn from real data and unexpected edge cases.
Phase 3: Train the experts
No matter how experienced our data annotators are and how tested the processes of our annotation services are, the team must undergo the guidelines deep learning stage to interiorize the details expected in the dataset. This involves studying the documentation and testing it with our experienced labelers, QA agents, and PM (Project Managers) into client-specific exams where they are presented edge cases, and we measure their resolution capacity.
Phase 4: Annotation and QA (Quality Assurance) pass
After every image is annotated, classified, or tagged, second independent QA analysts audit the first team's work and score it using a scorecard. Labelers with low scores are retrained, and their work is redone. Annotators with high scores are rewarded by an incentive system and progress to higher roles over time.
Phase 5: Delivery
Depending on the dataset volume, projects are split in smaller batches and delivered separately. Recurrent projects involve daily, weekly or monthly delivery packages. Output files can vary in format from JSON, GeoJSON, PNG Masks, JPG, TIFF, CSV, COCO, and others.
Scalable Teams, Elasctic Pricing
We range from teams of a few annotators involved in a computer vision project for a few days to hundreds of labelers in recurrent capacity for clients that require a continuous influx of training data into their ML models.
BUNCH counts with a number of FTEs that are labeling a large pipeline of projects that can be allocated to new projects depending on priorities. We also count with a pool of pre-trained experienced annotators ready to jump in as volume is needed.
The proven team structure is as follows:
Annotation Team
Support Team
Image Annotator
Workforce Manage
QA Analyst
Trainer
Team Lead
Project Manager
Our Computer Vision labeling experts are experienced in object recognition, 2D and 3D bounding boxes, as well in pixel segmentation, polygon annotation and keypoint annotation. We label and classify 2D and 3D objects, instruments, signs, tools, and bodies, including facial recognition and movement prediction for several R&D educational projects.
High-Accuracy Labeling Outsourcing Services
High-Touch Project Management
Receive instant response, feedback and support from our dedicated 24/5 account management team
Flexible Pricing
Get a custom plan with elastic pricing models that fit your annotation project volume, quality and urgency
Scalable Workforce
Our elastic workforce allows you to scale up from thousands to millions of images in hours
Accuracy Culture
Continuous training and rigorous QA complemented by double-pass techniques secure the highest accuracy
In-House Annotators
Our fully managed in-house annotation teams enable pixel-perfect accuracy and full compliance with your guidelines
Project Calibration
We will annotate a sample of your images and come back to you with proposed productivity estimates and quality thresholds
Dashboard Mastery
Our exposure to different dashboards enables us to handle high-volume and multi-user annotation at exceptional efficiency standards
Your Data is Yours
We permanently delete your datasets upon completion of milestones. Our in-house team is under strict NDA to protect your business confidentiality.
Compliance Above Standards
We meet international compliance standards for data handling and processing, security, confidentiality, and privacy
Kickstart your Image Annotation project in days, not weeks
Share your challenge with us and we will send you a quote personally in less than 24 hours.
Get a proposal in less than 24 hours
Image Annotation Services Pricing
- Full-time dedicated annotators
- Complimentary QA audits
- Custom shifts or 24/7
- High accuracy
- Instant scalability
- Priority projects available
We reinvented the outsourcing model with flexibility in mind.
We set full-time teams and work on one-time projects of all sizes.