From Task Code to Client Delivery

Hi, I’m Diya, a senior Data Processing Executive at LexData Labs. Outside of work, I’m a coffee enthusiast, a weekend hiker, and a firm believer that good music makes great focus. My current annotation playlist? Lo-fi beats mixed with old-school Bollywood classics.

Today, I want to take you behind the screen and share what it’s really like to label images for AI systems, not just the technical process, but the mindset, challenges, and little wins that come with being the human in the loop.

Morning Routine: Getting into the Flow

Most days begin with checking client emails, task updates, new batch alerts, or annotation feedback. We receive a task code that unlocks the dataset. Once we receive it, I load the task into CVAT, our annotation platform, where the images become visible and ready to label.  

To keep things organized, I also create an Excel tracker based on the dataset. It helps me divide the images among the team and keep our progress visible and easy to manage. Everyone knows which images they’re working on, and it’s easier for me to catch if anything’s been missed or needs rechecking.

Then it’s time to focus. I set up my desk with a warm mug of coffee, queue up my focus playlist, and settle into the rhythm of the day.

Little Life hack: I always start with a few simpler images to ease into the rhythm before tackling more detailed frames. Think of it as a warm-up for your brain.

Labelling in Action: Focused Time, Image by Image

From around 09:30 AM to 1:00 PM, I’m in deep focus mode, labelling images with care and consistency. On average, each image takes about 15–20 minutes, but that can double, depending on complexity.

Some scenes involve overlapping figures, motion blur, or challenging lighting, these take serious attention to get right. I'm not just drawing boxes or polygons. I’m teaching a machine to understand visual context, that shadows aren’t objects, and that accuracy matters pixel by pixel.

Field Challenge: I once had to label objects in a dusty farm field. The low visibility and poor contrast made annotation difficult, so i adjusted color contrast, saturation, gamma, and brightness to reveal hidden objects. At the same time, I reviewed frame by frame the entire project to understand the scene better before labeling. It was tricky, but finally getting it right? Super satisfying.

Midday Recharge: Lunch & Light Walk

Around 1:00 to 2:00 PM, I take my lunch break. It’s a good time to unplug, enjoy a quiet meal, maybe chat with teammates, and then step outside for a short walk. Stretching my legs and getting some sunlight always helps clear the mental clutter before the next work session.

Team QA: Script Check + Manual Review

Once a batch is labelled at 2:00 PM, we run a Python script that flags incomplete annotations or missing labels like a pre-flight checklist for our work. While it runs, I stretch, sip my tea, and scroll through the logs.

After this, I take a quick 15-minute break, a reset for the brain. My go-to move? A few rounds at the dartboard! It’s surprisingly effective at refreshing my focus and getting my hand-eye coordination back in sync.

Then it’s time for manual QA, where we catch anything the script misses, especially in messy or blurry images. This step can be slow, but it's crucial. We check edge alignments, overlapping objects, and background noise.

What Makes It Challenging (And rewarding)

This work requires patience and a strong eye for detail. There are days when it feels repetitive, but what keeps it exciting is knowing that every label we make contributes to smarter, safer AI systems in the real world.

Common question I get: “Will AI replace this job soon?”
Honestly, automation helps but it still takes people like me, with care and experience, to guide AI systems in the right direction. And I like it that way.

Wrapping It Up: Final Delivery

Once the team is confident the batch meets our quality standard, we deliver it to the client. Our output? Clean, detailed annotations that are ready for training and deployment.

Looking back on a batch of 500 images, it’s rewarding to know that every label was made with care and will directly improve the accuracy and reliability of AI models out in the world.

Why It Matters: The Real Impact of Human Annotation

·        Model accuracy improved 10% after implementing our structured annotation workflow

·        Lower development costs by catching errors early

·        Faster model deployment due to fewer annotation inconsistencies

·        Stronger trust in AI when the data is reliable

Automation is evolving fast, but at LexData Labs, we know it’s the human touch that makes good data great.

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Final Thought

Every dataset is a challenge, a story, and a step forward. We’re not just labelling. we’re building the bridge between the real world and machine vision.

Let’s talk again after the next 500 images. 👩‍💻

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