You will get Custom Computer Vision Model for Image Classification & Segmentation
Project details
Get a custom computer vision model built for your image data classification, segmentation, or feature detection trained and validated on your actual images, not a generic pretrained demo.
Ideal for:
• Medical & diagnostic image analysis (X-ray, MRI, scans)
• Product, defect, or quality classification
• Image segmentation (isolating objects, regions, or features)
• Custom image recognition for any industry-specific use case
What you'll receive:
✔ Data preprocessing cleaning, augmentation, and formatting for training
✔ CNN-based model built with PyTorch or TensorFlow, matched to your task
✔ Evaluation report: accuracy, precision/recall, and conusion matrix
✔ Clean, structured code your team can maintain or extend
I validate every model against realistic image variation lighting, angle, resolution not just a clean training set, so performance holds up on new images.
Recent example: built classification models for medical imaging (brain MRI, disease detection) and a multi-model segmentation pipeline for a production computer vision system.
Tell me what your images need to do classify, segment, or detect and I'll scope what's realistic for your data and timeline.
Ideal for:
• Medical & diagnostic image analysis (X-ray, MRI, scans)
• Product, defect, or quality classification
• Image segmentation (isolating objects, regions, or features)
• Custom image recognition for any industry-specific use case
What you'll receive:
✔ Data preprocessing cleaning, augmentation, and formatting for training
✔ CNN-based model built with PyTorch or TensorFlow, matched to your task
✔ Evaluation report: accuracy, precision/recall, and conusion matrix
✔ Clean, structured code your team can maintain or extend
I validate every model against realistic image variation lighting, angle, resolution not just a clean training set, so performance holds up on new images.
Recent example: built classification models for medical imaging (brain MRI, disease detection) and a multi-model segmentation pipeline for a production computer vision system.
Tell me what your images need to do classify, segment, or detect and I'll scope what's realistic for your data and timeline.
Machine Learning Tools
NumPy, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$100
|
Standard
$230
|
Advanced
$420
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$15
Additional Scenario
(+ 2 Days)
+$35
Source Code
(+ 1 Day)
+$20Frequently asked questions
About Shakeel
Applied Machine Learning & Computer Vision Engineer
Faisalabad, Pakistan - 2:26 pm local time
I design and build production AI systems not notebooks, not proofs of concept. My work sits at the intersection of computer vision and agentic AI: systems that perceive an environment and then decide what to do about it, deployed as services your team can actually maintain.
Computer Vision & Real-Time Detection
Object detection, image classification, and image segmentation pipelines built on YOLO, OpenCV, PyTorch, and TensorFlow tuned to your data, tested against real-world conditions, not benchmark sets.
Agentic AI & Automation
Multi-agent systems built with LangChain and LangGraph, combining perception with decision-making. RAG applications for document and knowledge retrieval. Deployed as standing services via FastAPI and Python inference pipelines that run continuously, not scripts you have to babysit.
Production Engineering
Model deployment, clean architecture, and documentation as standard practice the difference between a system that works in a demo and one that survives contact with production data.
One recent system: YOLOv8-based real-time object detection at 92–94% mAP, feeding a multi-agent LangGraph decision pipeline with end-to-end response under 2.5 seconds. It's patent-pending, with the underlying research currently under review at IEEE Access. Separately, a classification model shipped at 94% accuracy across 250,000+ real-world data points evidence that the work holds up outside a controlled environment.
If you're evaluating whether your AI initiative is technically sound before you commit budget to it, that's the right first conversation to have. Tell me what you're building, and I'll give you a direct assessment of what's realistic.
Steps for completing your project
After purchasing the project, send requirements so Shakeel can start the project.
Delivery time starts when Shakeel receives requirements from you.
Shakeel works on your project following the steps below.
Revisions may occur after the delivery date.
Client Sends Requirements
Image dataset, the task (classification, segmentation, detection), and target outcome.
Data Preprocessing
Clean, augment, and format images for training including handling class imbalance if present.