You will get your own custom object detection model
Project details
You will get a state of the art battle tested Object Detection model with useful documentation, jupyter notebooks, model weight files, related charts for metrics, training loss, val loss and a detailed pdf reports from the training and evaluations. You can choose your own framework among yolov5, yolov4, tensorflow3 and detectron2.
What's included
| Service Tiers |
Starter
$500
|
Standard
$700
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 3 | 6 | 10 |
Number of Graphs/Charts | 5 | 10 | 15 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$75 - $200Frequently asked questions
About Nurul
Expert fullstack developer with strong AI/ML experience
Dhaka, Bangladesh - 10:07 am local time
I’m a full-stack software architect with 15+ years of experience building intelligent, production-grade systems—6 of those years as CTO and Chief Architect, leading high-impact AI/ML initiatives from vision to execution.
I specialise in designing and deploying full-stack AI solutions that bridge machine learning with scalable software architecture. My expertise covers a wide range of AI applications, including:
Custom Object Detection Models for computer vision tasks (e.g., real-time detection, industrial automation, safety systems) using YOLO, Detectron2, and TensorFlow Object Detection API
Optical Character Recognition (OCR) for document automation, ID verification, and form processing
Speech Technologies: ASR (Automatic Speech Recognition), STT (Speech-to-Text), TTS (Text-to-Speech) using Whisper, Coqui, Mozilla TTS, etc.
Multimodal RAG (Retrieval-Augmented Generation) Agents, combining vision, language, and speech for next-gen AI assistants and knowledge tools
My stack includes PyTorch, TensorFlow, OpenCV, Hugging Face Transformers, LangChain, FAISS/Weaviate, Docker, and cloud-native DevOps (AWS/GCP/Kubernetes). I have built pipelines from data labelling and model training to deployment and MLOps—including fine-tuning models on proprietary datasets for specific business use cases.
Peers trust me for my ability to lead, collaborate, and execute with technical precision. Whether you need to architect a new AI-driven product or integrate intelligent models into existing workflows, I bring the clarity, speed, and hands-on experience to deliver results.
Steps for completing your project
After purchasing the project, send requirements so Nurul can start the project.
Delivery time starts when Nurul receives requirements from you.
Nurul works on your project following the steps below.
Revisions may occur after the delivery date.
A quick meeting to identify your use case
I will discuss your needs for this model and the nature of your training and evaluation data. To be more specific I need to identify your classes, data augmentations and network architecture.