I will accurately annotate images and videos for computer vision
Your Satisfaction is my Satisfaction
Level 1
Has met certain performance criteria and shows strong potential in the marketplace.
About this Gig
A computer vision model is only as good as the data it learns from. I can help you turn your raw images or videos into a clean, accurately labeled dataset that's ready for training.
I work with image and video annotation for computer vision projects, including object detection, classification, segmentation, and tracking. I can annotate your data from scratch or review an existing dataset and correct inconsistent or inaccurate labels.
What I can help you with:
- Bounding box annotation
- Polygon annotation
- Image segmentation
- Object detection datasets
- Video frame annotation and object tracking
- Image classification
- Dataset review and quality checking
- Train, validation, and test organization
- YOLO, COCO, Pascal VOC, and other required formats
Tools I work with:
CVAT, Roboflow, Label Studio, and other annotation tools depending on your project's requirements.
Since annotation workload can vary significantly depending on the number of objects, classes, frames, and complexity of the images, please message me before placing a large or complex order.
Send me a few sample images along with your labeling requirements, and I'll help you determine the right scope for the dataset.
Technique:
Manual
Tagging type:
Text
•
Image
•
Video
My Portfolio
FAQ
What annotation formats can you provide?
YOLO, COCO, Pascal VOC and other formats depending on your training pipeline.
Can you annotate videos?
Yes. I can work with video frames and object-tracking annotations. Since video projects vary considerably in size, please contact me first for a custom quote.
Can you work with my existing dataset?
Yes. I can review existing annotations, correct labeling errors and help organize the dataset for training.
Why should I contact you before placing a large order?
Annotation complexity depends on more than the number of images. Object density, number of classes, annotation type and video length can significantly change the workload. Seeing a small sample allows me to quote the project properly.
