You will get Custom Object Detection Model – Tailored AI Solutions
Top Rated
Top Rated
Project details
Need a model that can accurately detect people, products, vehicles, defects, or custom objects in real-world scenarios? I will build you a custom AI-based object detection solution, tailored to your unique dataset and use case.
Instead of sticking to one model (like YOLO), I choose the right architecture—YOLO11, Faster R-CNN, SSD, Detectron2, or other state-of-the-art frameworks—based on your goals (real-time detection, high accuracy, mobile deployment, etc.).
You’ll Get:
• Model trained on your data – completely custom
• Choice of best-fit architecture for your problem
• Deployment-ready model with easy-to-follow instructions
• Optional integration into your existing app/platform
• Source code for further use
Instead of sticking to one model (like YOLO), I choose the right architecture—YOLO11, Faster R-CNN, SSD, Detectron2, or other state-of-the-art frameworks—based on your goals (real-time detection, high accuracy, mobile deployment, etc.).
You’ll Get:
• Model trained on your data – completely custom
• Choice of best-fit architecture for your problem
• Deployment-ready model with easy-to-follow instructions
• Optional integration into your existing app/platform
• Source code for further use
Machine Learning Tools
Azure Machine Learning, Keras, NumPy, OpenCV, Python, PyTorch, SciPy, TensorFlow, Tesseract OCR, Vertex AIWhat's included
| Service Tiers |
Starter
$2,000
|
Standard
$4,000
|
Advanced
$6,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 2 | 2 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 0 | 0 | 0 |
Model Validation/Testing | |||
Model Documentation | - | - | - |
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$1,000
Additional Model Variation
(+ 4 Days)
+$1,500
Additional Scenario
(+ 7 Days)
+$2,000
Data Source Connectivity
(+ 3 Days)
+$1,000
65 reviews
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NL
Nathaniel L.
Sep 5, 2025
Senior AI developer
Great work! I'll contact you for next tasks in the future.
BK
Braum K.
Apr 14, 2025
Computer Vision consulting
What is there to say about Ahmer? Ahmer is one of the all time greatest colleagues I've ever had in my career. Not only is Ahmer creative, but he is brilliant, thorough, and a pleasure to work with. We have worked with Ahmer for several years, and each and every time we've worked together, he has gone so above and beyond. Ahmer is an amazing scientist, truly brilliant. He has the amazing ability to not only see the immediate problem, but to see several layers deep. Ahmer is absolutely one of my most trusted colleagues, and someone who I truly love to work with. If you have an opportunity to work with Ahmer, you absolutely should jump on it. A brilliant man, and a truly good person. A++++
NL
Nathaniel L.
Apr 14, 2025
Senior AI and ML Developer
Ahmer delivered good results, and I will use him for future tasks.
LS
Lincoln S.
Mar 22, 2025
Senior Product Manager - AI/ML Technologies
Amazing communication. Brilliant work. Fantastic freelancer. I highly recommend Ahmer.
JC
Javier C.
Sep 18, 2024
30 minute consultation
Great communication skills and solid knowledge in CV. Would consult with him again
About Muhammad Ahmer
AI Solutions Architect | RAG, AI Agents & Computer Vision
100%
Job Success
Lahore, Pakistan - 3:52 pm local time
Most computer vision, RAG, and search systems work great in a demo and quietly fall apart once real data, real users, and real edge cases show up. That's the gap I solve.
Where I deliver the highest value:
Production Computer Vision — Founding AI architect for a retail shelf-intelligence platform, from founding through five years of continuous deployment, now serving 200+ global FMCG brands including P&G — the platform received the 2025 P&G External Business Partner Excellence Award. Detection, tracking, OCR, visual search, and edge-to-cloud deployment across Raspberry Pi, Jetson (Nano/Xavier/Orin), Coral TPU, and Intel NCS2 — where the hardware chose the model rather than the benchmark.
Agentic RAG & Retrieval Systems — Built a legal research system end-to-end: ~15M tokens across ~1,600 documents, with a citation-strict generation layer where every claim carries its source, section, and page. Also built ShotDeck's search and tagging layer over its 3M+ image library — parallel GPU embedding, hybrid/semantic retrieval, and an auto-tagging classifier for cinematic vocabulary, at sub-second latency. My retrieval toolkit spans corrective RAG (CRAG), query rewriting, cross-encoder reranking, and GraphRAG, applied wherever the retrieval problem calls for it.
Claude-Native Agent Architecture — That citation-strict layer runs on Claude. I work with the Claude API, Claude Code, the Agent SDK, and MCP, designing tool governance and permission boundaries — PreToolUse/PostToolUse hooks, validation-retry loops, hierarchical CLAUDE.md configuration — so an agent's authority is auditable by construction rather than by hope. At Biscuit AI I led the migration to a coordinator/subagent multi-agent architecture, and built the evaluation framework that told us whether it was actually improving: groundedness, context relevance, answer relevance, because "it feels smarter" is not a metric.
Healthcare & Regulated Environments — Built a healthcare de-identification pipeline: OCR, a two-model biomedical NER ensemble, and coordinate-mapped visual redaction, achieving 85%+ PHI detection accuracy on diagnostic reports. At Edwards Lifesciences, I built PHI de-identification for cardiac echocardiograms. In clinical and legal work, an unsupported answer isn't a rounding error. It's a liability.
Full AI Ownership — I stay past the demo. Long after launch, I'm the one watching cost curves, retraining triggers, and model drift — the unglamorous work that decides whether a system is still accurate and affordable a year in. Architecture decisions made in month one either compound or collapse by month twelve. I design for month twelve.
The principle behind all of it: find the one requirement that makes a system unshippable — compliance, latency, cost, or scale — and design backward from it before touching a model.
If your AI project is stuck somewhere between a working demo and something you can put in front of real users, tell me what's breaking. I respond within 24 hours with what I'd fix first, and can have a scoped path to production for you within 48.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Ahmer can start the project.
Delivery time starts when Muhammad Ahmer receives requirements from you.
Muhammad Ahmer works on your project following the steps below.
Revisions may occur after the delivery date.
Review Dataset and Requirements
I will review the provided dataset and clarify detection goals or preferences.
Select and Configure Model Architecture
Based on your use case, I will choose and configure the most suitable detection model.