Khalid M.

5+ years of hands on experience I specialize in Image Annotation, Video Annotation, 3D LiDAR Annotation, Computer Vision datasets, and AI model evaluation for machine learning and deep learning projects. With years of production experience in AI data annotation and dataset quality control, I have worked on: ✅ Computer Vision training datasets ✅ Autonomous driving and mapping data ✅ Object detection and recognition projects ✅ Image classification and tagging ✅ Video tracking and frame-by-frame annotation ✅ 3D point cloud and LiDAR annotation ✅ LLM response evaluation and AI training tasks 🖼️ IMAGE ANNOTATION SERVICES I provide accurate: • Bounding Box Annotation • Object Detection Annotation • Polygon Annotation • Semantic Segmentation • Instance Segmentation • Image Classification • Image Tagging • Keypoint Annotation • OCR Data Annotation Tools: CVAT | Label Studio | Roboflow | Supervisely | LabelImg 🎬 VIDEO ANNOTATION & OBJECT TRACKING Professional video labeling services including: • Frame-by-frame annotation • Object tracking • Multi-object tracking (MOT) • Track ID management • Occlusion handling • Action recognition annotation • Event segmentation • Video classification 🧊 3D LiDAR & POINT CLOUD ANNOTATION Experienced with: • 3D Point Cloud Annotation • LiDAR Data Labeling • 3D Bounding Boxes • Cuboid Annotation • Object Detection in Point Clouds • Semantic Segmentation • Instance Segmentation • Sensor Fusion Data Output formats: KITTI | nuScenes | PCD | LAS/LAZ | PLY 🤖 AI TRAINING DATA & LLM EVALUATION I support AI companies with: • LLM Response Evaluation • AI Model Evaluation • RLHF Data Annotation • Response Ranking • Preference Ranking (A vs B) • Instruction Following Evaluation • Safety & Quality Review • Rubric-Based Scoring I evaluate AI responses based on: ✔ Accuracy ✔ Relevance ✔ Helpfulness ✔ Safety ✔ Completeness ✔ Reasoning Quality 🔍 DATASET QA & ANNOTATION QUALITY CONTROL Good annotation requires more than drawing boxes. I provide: ✅ Annotation QA reviews ✅ Dataset audits ✅ Label consistency checks ✅ Error identification ✅ Guideline improvement ✅ Edge-case analysis ✅ Quality reports I review datasets to find: Incorrect labels Missing annotations Inconsistent classes Annotation guideline issues Model training risks 🛠️ MY WORKFLOW Review your annotation guidelines and requirements Complete a calibration sample Annotate according to your schema Perform quality checks before delivery Document unclear cases and edge scenarios No guessing. No shortcuts. Every label follows your project requirements. ⚙️ TOOLS & PLATFORMS • CVAT • Label Studio • Roboflow • Supervisely • LabelImg • VGG Image Annotator • Excel / Google Sheets • Custom annotation platforms 📦 DELIVERY FORMATS • COCO • YOLO • Pascal VOC • JSON • CSV • XML • KITTI Why Hire Me? Many people can create annotations. Fewer understand how annotation quality affects machine learning model accuracy. I focus on: ⭐ Accurate labeling ⭐ Consistent annotation standards ⭐ Reliable QA processes ⭐ Clear communication ⭐ Production-ready datasets Send your project details, dataset size, annotation guidelines, and deadline. I can help you build clean, reliable AI training data.