You will get expert data annotation & labeling for computer vision (image/video)
Project details
Most annotators just draw boxes. I'm a computer vision engineer who labels your data the way a model actually needs to train, so you get accuracy, not just annotations.
I handle image, video, and action-recognition data across every annotation type: bounding boxes (object detection), polygon & segmentation masks, keypoints/pose, and classification. Tools: CVAT, Label Studio, Roboflow, plus custom scripts for any format.
What makes the difference: a clear label taxonomy, consistent guidelines, a dedicated QA pass to catch mislabels and edge cases, balanced classes, and a clean train/val split. You receive a model-ready dataset exported in your format (YOLO, COCO, Pascal VOC, or CSV/JSON) with documentation on how it was labeled.
Send me a few sample images and your target classes, I'll confirm the scope and the quality you can expect before you order.
I handle image, video, and action-recognition data across every annotation type: bounding boxes (object detection), polygon & segmentation masks, keypoints/pose, and classification. Tools: CVAT, Label Studio, Roboflow, plus custom scripts for any format.
What makes the difference: a clear label taxonomy, consistent guidelines, a dedicated QA pass to catch mislabels and edge cases, balanced classes, and a clean train/val split. You receive a model-ready dataset exported in your format (YOLO, COCO, Pascal VOC, or CSV/JSON) with documentation on how it was labeled.
Send me a few sample images and your target classes, I'll confirm the scope and the quality you can expect before you order.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Deeplearning4j, Google AutoML, Keras, MLflow, Open Neural Network Exchange, OpenCV, PyBrain, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$50
|
Standard
$150
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 1 day | 3 days | 7 days |
Number of Revisions | 1 | 1 | 1 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | |
Source Code | - | - | |
Taxonomy | - |
Frequently asked questions
About Muhammad Muneeb
Senior Computer Vision Engineer | Real-Time AI Video Analytics
Rawalpindi, Pakistan - 4:06 am local time
I build real-time AI systems that turn live video into actionable intelligence, whether that’s detecting fire in a facility, flagging suspicious behavior in retail, or analyzing player movement in sports.
Most computer vision models work in a notebook.
Mine run in production, on edge devices, in the cloud, or across multi-camera systems.
I specialize in designing full end-to-end pipelines:
• Data strategy and annotation design
• Model selection, training and fine-tuning
• Latency optimization (real-time performance)
• Multi-object tracking and action recognition
• Edge deployment (Jetson, TensorRT)
• Cloud APIs and scalable architecture
• Monitoring and performance improvement
🔎 What I Build
🚨 Surveillance & Safety AI
Real-time fire detection, shoplifting detection, intrusion monitoring, license plate recognition, behavioral analysis systems.
⚽ Sports Analytics
Player tracking, action recognition, pose-based performance analysis, automated highlight tagging, multi-camera tracking pipelines.
🏭 Industrial Vision
Defect detection, visual inspection systems, OCR pipelines, automated monitoring.
⚙ Technical Foundation
Deep Learning: PyTorch, TensorFlow
Detection & Tracking: YOLO, DeepSORT, ByteTrack
Action Recognition: SlowFast, R(2+1)D
Optimization: TensorRT, ONNX, Edge deployment
Backend & APIs: FastAPI, Flask
Cloud: AWS, GCP
Deployment: Docker, GPU inference pipelines
I don’t just train models.
I design production-ready AI systems that remain accurate under poor lighting, occlusion, motion blur, and real-world constraints.
If you are building:
• A smart surveillance system
• A sports analytics product
• A real-time AI camera application
• An edge-deployed vision system
Send me a brief description of your use case, and I’ll outline how I would architect your solution.
Let’s build something that works outside the lab.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Muneeb can start the project.
Delivery time starts when Muhammad Muneeb receives requirements from you.
Muhammad Muneeb works on your project following the steps below.
Revisions may occur after the delivery date.
Align on labels & guidelines
confirm your classes, edge-case rules, and output format.
Annotate your data
label in the right tool (CVAT, Label Studio, Roboflow) to consistent standards.