We build computer vision systems that see, read, and decide.
Rizwan AI builds computer vision and deep learning systems that turn images and video into reliable, automated decisions. Built to run in production on cloud, on-prem, or edge devices.
Computer vision, built for production
Deep learning across image and video, designed, trained, and deployed to run reliably in the real world.
Detect & count objects
Find and count objects in images and live video, in real time, even in busy scenes.
Track people & vehicles
Follow movement across frames to measure flow, paths, and dwell time.
Smart parking
Spot free and occupied bays and automate entry, exit, and enforcement.
Read documents (OCR)
Pull text, fields, and tables from invoices, IDs, and forms in any layout.
Classify & sort images
Label images at scale by category, condition, or defect type.
Video analytics
Turn camera feeds into counts, dwell time, and zone alerts.
Quality inspection
Catch scratches, misalignment, and missing parts on the production line.
Deploy & maintain
Run models reliably on cloud, on-prem, or edge, with monitoring and retraining.
Computer vision that ships to production
Rizwan AI builds computer vision and deep learning systems that turn images and video into reliable, automated decisions. We focus on getting them into production, not just a notebook.
We work end to end: framing the problem against your data, building a prototype you can evaluate early, then hardening it into a system that runs on cloud, on-prem, or edge hardware.
- End to end: problem framing, prototyping, deployment, and maintenance
- Object detection, tracking, OCR, video analytics, and quality inspection
- Runs where you need it: cloud, on-prem, or on-device at the edge
What clients say
Muhammad Rizwan Munawar has delivered computer vision and deep learning projects for clients worldwide. Here is what a few of them said about working together. Every review is verified on Upwork.
5 🌟 reviews onExcellent to work with and exceeded all expectations in answering questions related to various courses. One of the standout qualities of Muhammed was their commitment to going above and beyond. They didn't just provide brief, surface-level answers. Instead, they took the time to provide in-depth responses that demonstrated their expertise and dedication to the job
Muhammad did an excellent job on my project. He's very knowledgeable, intelligent, and highly capable. I hope to work with Muhammad again in the future.
Even though he had to work till midnight, Rizwan always worked diligently and attempted to solve difficulties. Rizwan is the best freelancer with whom I've ever worked in Upwork.
Good experience, was available when needed and on time on our appointment. explained well and teached well.
Work was completed promptly and with great care. He's willing to go the extra mile and was willing to adapt to changes. Great job!
Excellent to work with and exceeded all expectations in answering questions related to various courses. One of the standout qualities of Muhammed was their commitment to going above and beyond. They didn't just provide brief, surface-level answers. Instead, they took the time to provide in-depth responses that demonstrated their expertise and dedication to the job
Muhammad did an excellent job on my project. He's very knowledgeable, intelligent, and highly capable. I hope to work with Muhammad again in the future.
Even though he had to work till midnight, Rizwan always worked diligently and attempted to solve difficulties. Rizwan is the best freelancer with whom I've ever worked in Upwork.
Good experience, was available when needed and on time on our appointment. explained well and teached well.
Work was completed promptly and with great care. He's willing to go the extra mile and was willing to adapt to changes. Great job!
Hands-On AI: Computer Vision Projects with Ultralytics and OpenCV
A practical, project-based computer vision course where you build working applications with Ultralytics YOLO and OpenCV. You learn by doing and ship real solutions: object counting, queue management, tracking objects inside zones, analytical graphs from your detections, and a Streamlit app to run inference in the browser.
From the blog
Tutorials, code, and notes on computer vision, deep learning, and applied AI.
Video Depth Anything in Python: consistent depth for video
Depth Anything V2 run frame by frame flickers on video. Video Depth Anything fixes that with temporally consistent depth for long clips. Here is how to run it in Python, with the command line and a reusable script.
YOLO26 vs YOLO11 vs YOLOv8: which YOLO should you use?
YOLO26, YOLO11 and YOLOv8 side by side: what actually changed between them, the published accuracy and size numbers per variant, and a straight answer on which one to use for detection, edge deployment, or transfer learning.
How to extract text from images in Python (OCR): 5 libraries compared
A practical guide to reading text from images in Python: Tesseract, EasyOCR, PaddleOCR, docTR and VLM-based OCR, with runnable code and a which-one-to-pick guide.
How to use ByteTrack with YOLO for object tracking in Python
Give YOLO a memory. This tutorial uses ByteTrack to assign persistent IDs to objects across video frames, with a runnable Ultralytics script and a bytetrack.yaml tuning guide.
Build a semantic image search engine with CLIP and Python
Learn how to build a semantic image search engine that finds pictures by meaning. A few lines of Python turn a folder of images into a searchable index you can query in plain English.
YOLO26 vs YOLO11: Real-time ONNX FPS benchmark in Python
Build one small, reusable class that runs Ultralytics YOLO26 and Ultralytics YOLO11 as ONNX models, draws clean detections, and overlays live FPS and latency so you can compare their real-time speed on the exact same footage.
Depth Anything V2 in Python: image, video and webcam depth
Learn how to estimate depth from a single image, a video, or your webcam using Depth Anything V2 and a clean, reusable Python class, plus the run.py command, metric depth, and a note on TensorRT.
Ultralytics object trackers comparison: ByteTrack, BoT-SORT & More
How do the six Ultralytics trackers actually behave on the same footage? A look at BoT-SORT, ByteTrack, OC-SORT, Deep OC-SORT, FastTrack, and TrackTrack, their internals, trade-offs, and side-by-side results on ID switches, ID stability, and FPS.
Questions, answered
What kind of problems do you take on?
Do you build prototypes or production systems?
Where can the models run?
How much data do I need?
How does a project start?
Is my data kept private?
Have visual data going to waste?
Tell us what you want to see, read, or measure. Book a short call and we'll give you an honest read on feasibility, accuracy, and cost.