You will get AI-Powered Solar Panel Defect Detection & Instance Segmentation
Project details
I develop end-to-end Computer Vision solutions for object detection and instance segmentation using modern deep learning models such as YOLO. This project is ideal for clients who need an accurate, scalable, and production-ready AI solution for defect detection, industrial inspection, agriculture, or custom image analysis tasks.
I can help with the complete workflow: dataset review, annotation (CVAT), data preprocessing, model training and fine-tuning, performance evaluation, inference optimization, and deployment. Every solution is tailored to your dataset and business requirements to achieve the best balance between accuracy and speed.
You will receive a well-documented solution, clear evaluation metrics, and support throughout the project. Whether you need a proof of concept, a custom trained model, or a production-ready deployment, I focus on delivering reliable, maintainable, and high-quality Computer Vision systems.
I can help with the complete workflow: dataset review, annotation (CVAT), data preprocessing, model training and fine-tuning, performance evaluation, inference optimization, and deployment. Every solution is tailored to your dataset and business requirements to achieve the best balance between accuracy and speed.
You will receive a well-documented solution, clear evaluation metrics, and support throughout the project. Whether you need a proof of concept, a custom trained model, or a production-ready deployment, I focus on delivering reliable, maintainable, and high-quality Computer Vision systems.
Machine Learning Tools
MLflow, NumPy, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorchWhat's included
| Service Tiers |
Starter
$80
|
Standard
$250
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - |
Optional add-ons
You can add these on the next page.
Dataset Annotation (CVAT)
(+ 2 Days)
+$50
ONNX Export
(+ 2 Days)
+$50
Docker Deployment
(+ 2 Days)
+$100Frequently asked questions
About Isabek
Computer Vision & Machine Learning Engineer | YOLO, Python, OpenCV
Almaty, Kazakhstan - 9:10 am local time
I help businesses automate visual inspection, object detection, tracking, and image analysis using modern Computer Vision and Deep Learning technologies. From dataset preparation and annotation to model training, optimization, and deployment, I deliver complete end-to-end AI solutions tailored to real-world applications.
🔹 Computer Vision & AI Services
I can help with:
✅ Object Detection (YOLOv8 / YOLOv11)
✅ Instance & Semantic Segmentation
✅ Multi-Object Tracking (DeepSORT)
✅ Image & Video Processing
✅ Drone Vision & Aerial Inspection
✅ Industrial Inspection & Quality Control
✅ Dataset Annotation (CVAT)
✅ Model Training & Fine-Tuning
✅ ONNX Export & Inference Optimization
✅ Docker Deployment & REST API Integration
🔹 Technologies
• Python
• PyTorch
• OpenCV
• YOLO
• DeepSORT
• FastAPI
• Docker
• ONNX
• PostgreSQL
• Redis
• MLflow
⭐ Why Clients Choose Me
✔️ End-to-end Computer Vision development
✔️ Production-ready AI solutions
✔️ Clean, maintainable, and well-documented code
✔️ Strong communication and on-time delivery
✔️ Focus on accuracy, performance, and scalability
Whether you need a custom Computer Vision model, an AI-powered inspection system, or a production-ready deployment, I’m ready to help.
Let’s build your next AI solution together.
Steps for completing your project
After purchasing the project, send requirements so Isabek can start the project.
Delivery time starts when Isabek receives requirements from you.
Isabek works on your project following the steps below.
Revisions may occur after the delivery date.
Images or Video Dataset
Please upload the images or videos for analysis or model training. Include sample data if the full dataset is large.
Project Requirements
Describe your objective, expected output, defect classes, and any accuracy or deployment requirements.