You will get an object detection model using YOLO and Python
Project details
You will receive a custom object detection system trained specifically for your use case using modern computer vision techniques.
I develop detection models using the YOLO architecture and the Ultralytics framework. The system is trained on labeled datasets to accurately detect and localize objects in images or video streams.
This solution can be used for many applications such as traffic monitoring, product detection, security surveillance, industrial inspection, and smart analytics systems.
The workflow includes dataset preparation, image annotation, training the detection model, evaluating performance using metrics such as precision, recall, and mAP, and delivering a trained model with example detection results.
My goal is to build reliable computer vision models that can be easily integrated into real-world applications for automated visual analysis.
I develop detection models using the YOLO architecture and the Ultralytics framework. The system is trained on labeled datasets to accurately detect and localize objects in images or video streams.
This solution can be used for many applications such as traffic monitoring, product detection, security surveillance, industrial inspection, and smart analytics systems.
The workflow includes dataset preparation, image annotation, training the detection model, evaluating performance using metrics such as precision, recall, and mAP, and delivering a trained model with example detection results.
My goal is to build reliable computer vision models that can be easily integrated into real-world applications for automated visual analysis.
Machine Learning Tools
OpenCV, Python, PyTorchWhat's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 9 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Image annotation for object detection
(+ 2 Days)
+$50
Train additional object classes
+$50Frequently asked questions
About Prashant
AI Engineer | RAG Chatbots, LLM Apps, Computer Vision | Python
Delhi, India - 10:28 am local time
I design and deploy AI systems such as RAG chatbots, document intelligence tools, recommendation engines, and computer vision pipelines that automate information retrieval and decision-making.
Companies often struggle to extract useful insights from large amounts of information such as PDFs, internal documentation, or customer data. I help solve this by building AI systems that can understand, retrieve, and generate accurate responses using modern language models.
Background & Experience
• ~2 years of hands-on experience building AI and machine learning systems for real-world applications
• Developed RAG-based AI assistants capable of answering questions from documents and knowledge sources
• Built generative AI pipelines using Stable Diffusion for prompt-based image generation and visual transformations
• Implemented computer vision systems using YOLOv8 for object detection and scene understanding
• Designed production-style ML systems including recommendation engines, AI APIs, and modular ML pipelines
• Experienced in building scalable AI backends using Python, FastAPI, and modern ML frameworks
What I can help you build
• AI chatbots trained on company documents, PDFs, and knowledge bases
• RAG systems that allow users to ask natural questions and retrieve accurate answers from internal data
• AI assistants integrated into SaaS products, internal tools, or customer support platforms
• Intelligent search systems powered by embeddings and vector databases
• Scalable backend APIs for AI applications using Python and FastAPI
Technologies I commonly work with
Python, FastAPI, LangChain, vector databases, embeddings, and modern large language models.
I focus on building AI systems that are reliable, scalable, and easy to integrate into existing products.
If you're planning to build an AI chatbot, document assistant, or any LLM-powered feature, feel free to reach out. I’d be happy to discuss your project and help design a practical AI solution that fits your product and goals.
Steps for completing your project
After purchasing the project, send requirements so Prashant can start the project.
Delivery time starts when Prashant receives requirements from you.
Prashant works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset review and class definition
I review the dataset and confirm the object classes that need to be detected before preparing the training pipeline.
Image annotation and dataset preparation
Images are labeled using annotation tools and converted into YOLO format with proper train, validation, and test splits.