Klatch undertook an image segmentation project involving annotating a vast image dataset with planar and instance segmentation. Planar segmentation required classifying surfaces into three categories: vertical, horizontal, and others. Instance segmentation demanded identifying and labeling all objects specified in the client’s guidelines within each image. The unique challenge was the high variability within the dataset. Each image contained various surfaces at diverse angles, different object configurations, and complex backgrounds, making this an exceptionally demanding annotation task. Time constraints were also strict.

Internet/ Technology
Use case
Research & Development


The project presented several vital challenges that Klatch needed to overcome:

Image Complexity: Each image in the dataset was unique, featuring varying numbers of surfaces with diverse angles and object configurations. This complexity made the annotation process intricate and time-intensive. Annotators needed a deep understanding of spatial relationships and object boundaries to segment the images accurately.

Time Constraints: The project had a strict deadline, underscoring the urgency and importance of our work. This necessitated a highly efficient and scalable approach to annotation. Klatch needed to develop a workflow that could process a large volume of images quickly while maintaining accuracy.

Precision Requirements: The client demanded the highest annotation precision, as minor inaccuracies could significantly impact downstream processes. For instance, a mislabeled object could lead to a potentially dangerous situation in an application using this data for autonomous navigation. Klatch needed to ensure the annotations were accurate down to the pixel level.


To tackle these monumental challenges, Klatch implemented a comprehensive, multi-pronged solution leveraging its deep expertise in annotation and process optimization:

Specialized Annotation Team: Klatch has meticulously assembled a team of highly skilled and experienced annotation specialists, each proficient in complex computer vision tasks like planar and instance segmentation. These specialists have also undergone additional training specific to the client’s project requirements, including the types of surfaces and objects in the dataset. This adaptability ensures the highest level of precision in our services, tailored to your unique needs.

Enhanced Training and Optimization: The annotation team meticulously studied the project guidelines, grouping and reorganizing them into workflow-optimized modules for efficient knowledge transfer and faster processing. They developed clear examples, decision flows and documentation to minimize errors and ambiguity. Workflow optimizations and QA strategies were implemented for this project. The team also analyzed common errors during the pilot phase and implemented strategies to minimize their occurrence, ensuring timely project completion.

Multi-Stage Quality Control: A multi-stage quality control process was implemented with rigorous reviewing mechanisms at each stage. Our senior annotators conducted the first comprehensive review, bringing their extensive expertise to the table. This was followed by a second stage in which a dedicated quality assurance team inspected each image in detail, utilizing automated and manual processes to identify and rectify any inconsistencies or errors, no matter how minute. Identified errors were reworked, and complex cases were escalated to annotators for resolution.

Scalable Workforce Management: Klatch’s unique ability to rapidly scale its annotation workforce has been a critical factor in our success. This adaptability allows us to ramp up resources as needed, ensuring timely project completion without compromising quality. Klatch’s established network of vetted annotators allows quick team expansion while maintaining the required skill level, providing you with a flexible and reliable service, giving you peace of mind about the adaptability of our service.


Despite the tremendously complex nature of this project and tight timelines, Klatch successfully delivered all 2,000+ annotated images on time while exceeding the client’s precision benchmark of 98%. The meticulously segmented dataset gave the client a high-quality foundation for machine learning initiatives.

The key highlights include:

Timely Delivery: Klatch completed the project within the stipulated timeline, demonstrating its commitment to meeting deadlines. This ensured the client could stay on track with their development roadmap.

High-Precision Annotations: Klatch’s multistage QC process ensured that the delivered annotations were of the best precision, enabling seamless integration into the client’s downstream processes. This minimized the need for further data cleaning or refinement by the client’s team.

Cost-Effective Solution: Klatch’s efficient workflow, optimized processes and ability to rapidly scale our workforce in a calibrated manner enabled cost-effective project execution, providing exceptional value to the client. This included leveraging automation and optimizing the annotation tool for faster processing.

Continuous Improvement: The team consistently analyzed processes to identify optimization opportunities, implementing methods like annotator specialization and automated quality checks.

Klatch’s successful completion of this complex image segmentation project demonstrates its expertise in handling large-scale annotation tasks with high precision requirements. By leveraging a skilled workforce, optimized workflows, and rigorous quality control processes, Klatch delivered a valuable dataset that will empower the client’s AI or machine learning project. The project also highlights the importance of precise image segmentation in various computer vision applications.