You will get a custom object detection model on your data
Top Rated
Top Rated
Project details
I will train a custom YOLO object detection model on your data so it reliably finds the objects you care about, and I will prove it with real numbers, not guesses. Most detectors fail because of the dataset and the labels, not the model, so I start there. I review your images and annotations, fix what hurts accuracy, balance the classes, and augment where it helps. Then I fine-tune YOLO (v8 or v11), cut false positives and missed detections, and measure precision, recall and mAP on a held-out test set so you can see exactly how it performs. You get the trained weights and a clean inference script you can run yourself, with clear documentation of the process and results. Higher tiers add ONNX export and full edge or cloud deployment so the model is production-ready, not just a notebook. I have shipped detection systems in retail, security, manufacturing and sports, including on edge hardware, so I know where accuracy and speed break in the real world. Tell me your objects and share your images, and I will get you a working, measured detector.
Machine Learning Tools
Amazon SageMaker, Deeplearning4j, Google Sheets, NumPy, NVIDIA AI Platform, OpenCV, pandas, PyMC, Python Scikit-Learn, PyTorch, SciPy, TensorFlow, Tesseract OCR, XGBoostWhat's included
| Service Tiers |
Starter
$300
|
Standard
$600
|
Advanced
$1,300
|
|---|---|---|---|
| Delivery Time | 5 days | 5 days | 7 days |
Number of Revisions | 3 | 3 | 5 |
Number of Model Variations | 4 | 4 | 4 |
Number of Scenarios | 4 | 5 | 5 |
Number of Graphs/Charts | 6 | 6 | 6 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Frequently asked questions
7 reviews
(7)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
JG
Julius G.
Aug 12, 2026
Computer Vision Project
Outstanding work from start to finish. Jamal consistently went above and beyond, doing whatever was needed to make the project successful. Communication was excellent through messages and video calls, and they were always responsive, supportive, and willing to go the extra mile. Highly recommended.
KJ
Kunal J.
Jun 11, 2026
Lotiq destination use case coding
Jamal and the Sovavis team are excellent partners for the computer vision work we had in front of us. His team, especially Najam demonstrated strong ownership throughout the project, ensuring clear communication, timely updates, and proactive problem-solving. He was highly responsive and adaptable, especially when requirements evolved, and consistently focused on delivering high-quality outcomes.
From a technical standpoint, the team showed solid expertise and attention to detail, particularly in handling a computer vision code optimization, edge cases, and rigorous testing. What stood out most was team’s commitment to understanding the broader product vision—not just executing tasks, but contributing thoughtful suggestions to improve the solution.
Overall, a reliable and professional team that I would strongly recommend for complex, end-to-end product and engineering engagements. Looking forward to working together again.
From a technical standpoint, the team showed solid expertise and attention to detail, particularly in handling a computer vision code optimization, edge cases, and rigorous testing. What stood out most was team’s commitment to understanding the broader product vision—not just executing tasks, but contributing thoughtful suggestions to improve the solution.
Overall, a reliable and professional team that I would strongly recommend for complex, end-to-end product and engineering engagements. Looking forward to working together again.
SD
Sofia P D.
Apr 28, 2026
Computer Vision Engineer for Object Detection Project
Muhhamad was very proactive and devoted to fulfilling the requirements of the project working in a diligent and effective way to achieve it. Without a doubt I will hire him again!
RJ
Roy J.
Mar 2, 2026
CV Measurement Pipeline Expert Needed
This guy knows what he's doing. In our engagement, he was upfront, knowledgeable, and took the time to make sure I was taken care of. If you're looking to work with a pro in CV, look no further.
AK
Ajay K.
Feb 13, 2026
Computer Vision Engineer – Object Tracking & Counting from Video
Jamal was supportive and remained engaged throughout the duration of the project, making sincere efforts to assist us until the very end. While there were a few areas that could have been further refined, the overall collaboration was positive and professional.
It was good working with Jamal, and I would recommend him for future engagements.
It was good working with Jamal, and I would recommend him for future engagements.
About Muhammad
Computer Vision Engineer | Real-Time Video Analytics, Object Detection
100%
Job Success
Islamabad, Pakistan - 6:22 am local time
Most computer vision projects look good in a demo and then fall apart in the real world, when the lighting changes, the camera moves, or the feed is noisy. I build for the opposite. My focus is systems that stay accurate in real conditions, run in real time, and actually ship to production.
WHAT I BUILD Object detection and multi-object tracking with YOLO, RF-DETR, ByteTrack and BoT-SORT. Face and person re-identification across multiple cameras. Pose estimation and movement analysis on video. OCR and document AI pipelines, including handwriting and structured extraction. Real-time inference optimised with TensorRT and ONNX for edge and cloud. Full delivery, from the trained model to a FastAPI service, a WebSocket stream, or a web dashboard your team can actually use.
SOME OF WHAT I HAVE SHIPPED A real-time drowning-detection system on NVIDIA Jetson, running on site at sub-100ms latency. A multi-camera person tracking and re-identification pipeline running in production on AWS. Rubric.pk, a live document-AI product that reads handwritten answers with OCR and an LLM, with paying users. A smart-fridge retail billing system that tracks products and reads tags to build a live bill. Industrial defect detection on constrained edge hardware, X-ray void detection on electronic boards, and food-manufacturing inspection. RAVE, an iOS app with on-device pose estimation, live on the App Store.
HOW I WORK I start by understanding your hardware, data and latency targets before writing code. I pick the right model for the job instead of forcing one approach, benchmark accuracy and speed at each step, and hand over clean, documented code your team can maintain. When a detection or document is uncertain, I design the system to flag it for review rather than guess, which matters for anything used in the real world.
COMMON QUESTIONS Can you work with our existing model or dataset? Yes. I can optimise, retrain, or extend what you already have. Edge or cloud? Both. I deploy on Jetson, Raspberry Pi and Hailo at the edge, and on AWS or GCP in the cloud. Can you take a proof of concept to production? That is one of my most common projects, hardening and deploying a working demo for real use. Do you handle the app and API too? Yes. I build the FastAPI services and the web or mobile front end so the model becomes a usable product.
INDUSTRIES Retail and logistics, security and surveillance, healthcare and safety, manufacturing and quality control, sports analytics, and education.
I am Top Rated with a 100 percent job success score, and I run a small engineering team at SovaVis, so I can take on larger end-to-end builds as well as focused, well-defined tasks.
If you need computer vision that works outside the lab, on real footage and real hardware, send me a message with a bit about your project and I will tell you honestly how I would approach it.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Review dataset
Review your images and labels, and agree the classes and the success metric.
Clean and Set Data
Clean and balance the dataset and add augmentation where it helps.