Maham N.

Available Now I'm an AI data annotator with hands-on experience in computer vision data labeling for machine learning teams. I work inside CVAT, Labelbox, Roboflow, and Label Studio daily, handling everything from image annotation and video annotation to complex segmentation tasks — so your engineers can focus on the model, not the data. My work covers the full range of computer vision annotation: bounding box annotation for object detection, semantic segmentation and instance segmentation masks, polygon annotation for irregular shapes, keypoint labeling for pose estimation, and frame-by-frame object tracking in video datasets. Each label follows your guidelines exactly — and when I hit an edge case, I flag it instead of guessing. I support machine learning and AI development teams who need clean, consistent training data at scale. Whether you have 500 images or 500,000, the quality standard stays the same — because one bad label in a batch affects everything downstream. ✅ What I bring to every project: 🔹Pixel-accurate segmentation masks with no sloppy edges 🔹Consistent bounding boxes across large object detection datasets 🔹Reliable video annotation with proper object tracking between frames 🔹Fast turnaround with clear communication at every stage 🔹A annotator who reads the labeling guide before touching the first image ➡️ Let’s Work Together Send me your dataset, annotation guidelines, or a sample task. I'll return a labeled batch so you can judge the quality yourself before committing to a full project.