Solutions · Data collection
Real-world data, collected to your spec
Video, images, audio, and text captured by vetted contributors in the places, conditions, and devices your model needs, with consent recorded for every file. Each upload is checked as it arrives, so a bad capture is redone right away instead of turning up at labeling time.
Overview
What a collection program actually involves
A model can only learn what it has seen. When the conditions it will meet in the field — a region, a time of day, a kind of weather, a camera angle — are missing from its training data, it fails there first. Collection programs exist to close those gaps on purpose: instead of training on whatever footage happens to be lying around, you decide what the model needs and go get it.
Doing that at scale is mostly an operations problem. You need contributors in the right places, with the right equipment, who will follow instructions exactly. You need every capture to match the same spec even though most contributors aren’t professionals. And you need to know a capture is bad the day it’s recorded, not weeks later when someone opens it to label. A shaky or mis-mounted clip found late means a reshoot, and some conditions — a particular sea state, fog at sunrise — don’t come back on schedule.
MLtwist runs collection as a managed program. We write the capture spec with you, match vetted contributors from our network to routes and regions, and approve each contributor’s setup from photos and a test clip before any full session is recorded. Every upload is checked automatically for resolution, frame rate, stability, and visibility, so problems are fixed while the contributor is still in the field. Reviewers confirm coverage, and each file is tagged by condition and delivered with a Data ID Card that records where it was captured, who owns it, and how it can be used.
Collection makes sense when the data you need doesn’t exist yet, or exists but not from the viewpoint, place, or conditions your model will face. It pairs well with synthetic data generation for the cases no one can safely film, and with labeling when the footage needs annotations before it’s useful.
The problem
Why this is hard to do well
The right people, in the right places
Coverage is only as good as the people recording it. You need contributors in specific regions, with the right equipment and the know-how to use it — drivers who will follow an assigned route, boat operators who can read a sea state and position a camera safely.
Consistency across amateurs
Most contributors aren't professional camera operators. Thousands of captures still have to share the same camera height, angle, resolution, and frame rate, or the model learns the differences between devices instead of the scene.
Problems found too late
A shaky, mis-mounted, or obstructed capture usually isn't noticed until someone opens it to label. By then the contributor has moved on, and a reshoot may mean waiting for the same weather, light, or sea state to come back.
How it works
From a spec to a delivered dataset
Collection is a logistics program as much as a data one. We run it end to end, and every step is designed to catch problems while they're still cheap to fix.
- 01
Write the capture spec
Your model's needs become a written spec: which regions, times of day, and weather, which device and mounting, what resolution and frame rate, and how much footage of each condition. The spec is what every later check measures against, so it's settled before anyone records.
- 02
Match contributors
Vetted contributors from MLtwist's network are matched to routes, regions, and schedules. That can mean drivers assigned to specific commutes and time windows, or experienced boat operators and coastal observers who know local waters and safety protocols.
- 03
Approve the setup first
Contributors follow a mounting guide, then send setup photos and a short test clip for approval. No one records a full session until the camera position, framing, and settings match the spec — a few minutes of checking up front instead of hours of unusable footage.
- 04
Check every upload
Uploads are checked automatically for resolution, frame rate, stability, and visibility as they arrive. Failures are flagged right away, so the contributor can re-record while they're still on the route and the conditions still hold.
- 05
Tag and deliver
A mix of automated checks and human review confirms coverage and categorizes each file by conditions such as weather, visibility, and activity. Footage is anonymized where the program requires it, and each file carries a Data ID Card before delivery.
Case study · Automotive company
Nationwide commute video, shot the same way in every car
An automotive company needed everyday driving footage from metropolitan, suburban, and rural regions of the U.S. — rush hour, midday, evening, and night, in rain, fog, overcast skies, and bright sun — recorded from one exact windshield height and angle to match its in-vehicle sensors.
MLtwist recruited a network of vetted drivers and assigned each one routes and time windows. Drivers sent setup photos for approval before recording, and every upload was checked automatically for resolution, frame rate, stability, and road visibility, so failed routes were re-recorded without delaying the program. Automated checks and human review then verified route accuracy, camera position, and conditions.
The result was a large, structured dataset with a consistent perspective across vehicles and coverage across geography, weather, and time of day. Early quality checks kept the share of unusable footage low, and the client received production-ready training data it could use to strengthen its perception models.
- high-resolution video from one fixed camera position
- 30 fps
- times of day, from morning rush hour to night
- 4
What you get
A dataset you can check for coverage
Collected data arrives ready to use, not as a pile of uploads.
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Footage to spec
Every file meets the resolution, frame rate, and framing in your spec, or it was re-recorded. You don't sort good captures from bad ones on your side.
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Condition tags
Each file is tagged by attributes such as time of day, weather, visibility, and activity. Gaps in coverage show up as numbers before training, not as model failures after it.
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Rights on record
A Data ID Card per file records capture location, ownership, associated contracts, and usage rights. When someone asks whether you can train on a clip, the answer is already written down.
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Labeled if you need it
Collected data can flow straight into labeling, with the same review and QA as data you bring. One program, one delivery, no hand-off between vendors.
FAQ
Common questions
What can you collect?
Video, images, audio, and text. Past programs include nationwide commute video recorded from a vetted driver network and ocean footage captured from vessels, shorelines, and elevated coastal positions across U.S. waters.
Who are the contributors?
Vetted people from MLtwist's network, chosen for location and skill rather than availability alone. For driving programs that meant drivers matched to specific routes; for ocean footage, experienced boat operators and coastal observers familiar with sea conditions and safety.
How do you keep thousands of captures consistent?
Written capture protocols, setup photos and test clips approved before full sessions, and automatic checks on every upload. Anything off spec — wrong resolution, unstable framing, a blocked view — is flagged right away and re-recorded.
How do you make sure we get every condition we need?
Coverage is planned, not left to chance. Contributors get recording schedules that assign time windows and conditions, such as sunrise, midday, sunset, and night, or clear, overcast, rain, and fog. Every delivered file is categorized by condition, so you can check the balance yourself.
What about privacy and consent?
Contributors agree to how their data is used, and each file's Data ID Card records ownership, contracts, and usage rights. Footage is anonymized where the program requires it — in the ocean program, locations, vessels, and people had to stay unidentifiable.
Can collected data be labeled too?
Yes. Collected footage can go straight into labeling by our team, yours, or both, with the same QA and Data ID Card as any other data. In the ocean program, MLtwist also pre-tagged footage so relevant segments could be filtered automatically.
Talk to us about data collection
Tell us the data type, volume, and timeline. We'll scope it with our team, yours, or both — and deliver it versioned, in your format.
Also available through Carahsoft and Google Cloud Marketplace.