You will get Custom Object Detection Model – Tailored AI Solutions
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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
Computer Vision Engineer | Medical Imaging & Multimodal AI | Edge AI
100%
Job Success
Lahore, Pakistan - 12:16 pm local time
The common thread across 80+ production deployments is a single architectural principle: isolate the binding constraint before any model decision — HIPAA compliance in clinical pipelines, sub-second retrieval across 3M+ cinematic assets, compute efficiency at 2M+ API calls/year, real-time inference on resource-constrained edge hardware. I own the full stack from data strategy through deployment: design, training, optimization, and production maintenance.
My hands-on execution spans five specialized, production-proven domains:
◆ COMPUTER VISION (11 years in production)
Built Shelfr's complete retail shelf-intelligence platform from zero — SKU detection, planogram compliance, OOS recognition, share-of-shelf — scaling to 200+ global FMCG brands including P&G. Awarded the 2025 P&G External Business Partner Excellence Award. Built face anti-spoofing and biometric verification systems at FOO Technologies — 90%+ accuracy against video replay, printed face, and mask attacks; document tampering detection at 91%.
Deep specialization: YOLO v5–v11 · YOLO-World · Grounding DINO · SAM / SAM 2 · Vision Transformer (ViT · Swin) · ByteTrack · OpenCV · instance & semantic segmentation · object detection & tracking · face recognition & anti-spoofing · pose estimation · few-shot & zero-shot learning · anomaly detection.
◆ HEALTHCARE AI & MEDICAL IMAGING (HIPAA · DICOM · FHIR · Clinical NLP)
Delivered production clinical AI for Edwards Lifesciences — HIPAA-compliant DICOM de-identification via custom U-Net, echocardiogram analysis, beat-to-beat LVEF assessment, and Attention-Based MIL for weakly labeled studies. Production medical NLP achieving 85%+ PHI detection accuracy across clinical notes, FHIR documents, and lab tables — context-aware obfuscation preserving clinical utility while eliminating re-identification risk. Late-interaction retrieval (ColPali/ColBERT) for radiology and pathology records in regulated environments.
Deep specialization: MONAI · DICOM · FHIR · nnU-Net · MedSAM · PHI de-identification · BioBERT · ClinicalBERT · medical image segmentation · biomedical NLP · EHR integration · HIPAA/GDPR compliance.
◆ MULTIMODAL AI & CROSS-MODAL RETRIEVAL
Architected Shotdeck's production retrieval system across the world's largest cinematic library — 3M+ assets — delivering sub-second natural-language queries via hybrid pgvector retrieval with HNSW indexing on 100+ parallel GPUs. Built any-modality search supporting dialogue, reference images, ambient audio, and video clip inputs — with 76 structured cinematographic tags across 15 categories.
Deep specialization: CLIP · DINOv2 · LLaVA · Qwen VLMs · Florence-2 · ImageBind · ColPali · ColBERT · Whisper · multi-vector retrieval · late-interaction token-level matching · image-text-audio-video embeddings.
◆ GENERATIVE AI · RAG & AGENTIC SYSTEMS
Built the full generative AI stack at Synthesys AI Studio — talking avatars, AI lipsync, voice cloning, text-to-image — scaled to 2M+ API calls/year; reduced compute costs from $100K+/year to a fraction via serverless architecture (Modal, Replicate). AI Humans product integrated into Canva. Production RAG and agentic systems at Biscuit AI: hybrid search, multi-vector reranking, HyDE, LangGraph state machines, CrewAI and AutoGen multi-agent orchestration, long-term memory (semantic, episodic, procedural), LangSmith evaluation and hallucination monitoring.
Deep specialization: GPT-4o · Anthropic Claude · Llama 3 · Mistral · Gemini · DeepSeek · LangChain · LangGraph · LangSmith · CrewAI · AutoGen · MCP · function calling & tool use · RAG · agentic RAG · LoRA/QLoRA fine-tuning.
◆ EDGE AI & EMBEDDED DEPLOYMENT
Deployed real-time inference across forklift safety and construction monitoring (Canaryaware), ATM anti-skimming, and multi-animal behavioral tracking — QAT-optimized architectures maintaining low-latency detection under industrial conditions (low light, occlusion, motion blur) within edge-cloud hybrid systems with centralized continuous learning.
Deep specialization: NVIDIA Jetson (Nano · TX1 · TX2 · Xavier · Orin) · Google Coral TPU · Raspberry Pi · Intel NPU / OpenVINO · TensorRT · ONNX Runtime · LiteRT · INT8/QAT quantization · model pruning · TinyML.
Building a medical imaging platform, multimodal retrieval product, production RAG system, or edge deployment? Message me with your use case or the bottleneck you're hitting — I respond within 24 hours and can typically set a discovery call within 48 to map your fastest path to production.
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.