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I will build custom computer vision, yolo, and deep learning models
Software Developer
Full-Stack & AI Engineer | Scalable Systems & AI Integrations
Specialized in building robust web applications and integrating advanced AI/LLM solutions into enterprise workflows.
• Web & Backend: An...
About this Gig
Off-the-shelf AI models often fail in real-world commercial environments. As a Full-Stack and AI Engineer, I develop production-ready Computer Vision systems and deep learning models tailored to your specific dataset and business logic.
Core Computer Vision Capabilities:
- Object Detection & Tracking: Custom YOLO model training for real-time monitoring and automation.
- Image Classification: Advanced deep learning pipelines using PyTorch and TensorFlow.
- OCR & Text Extraction: Automated document parsing and data digitization.
- Face Detection & Recognition: Secure biometric and visual analytics solutions.
- Image Processing: Custom OpenCV pipelines, dataset preparation, and annotation guidance.
The Full-Stack Advantage: I do not just deliver standalone Jupyter notebooks. I build complete AI architectures:
- Clean, scalable, and well-documented Python source code.
- API integration (FastAPI/Flask) to connect vision models to your existing software.
- Cloud and Edge deployment readiness (Docker, AWS, REST architectures).
Please send a direct message with your project scope and dataset availability before placing an order to evaluate technical feasibility and define the best architecture.
APIs:
Amazon Rekognition
•
Google Cloud Vision API
•
Azure Face API
Programming language:
Python
•
R
•
SQL
•
Java
•
NoSQL
Tools:
Jupyter Notebook
•
OpenCV
•
TensorFlow
•
MLflow
•
CVAT
•
Colab
•
PyTorch
Frameworks:
Scikit-learn
•
Google ML Kit
•
Keras
•
PyTorch
•
Panda
My Portfolio
FAQ
Do I need to provide a labeled dataset for custom YOLO model training?
Not necessarily. If you have raw images or videos, I can guide you on defining annotation specifications or setting up automated pre-annotation pipelines. If you already have labeled data (YOLO, COCO, or Pascal VOC format), we can proceed directly to training.
Do I get full ownership of the source code and model weights?
Yes. Upon order completion, you receive 100% intellectual property rights. This includes the trained model weights (.pt, .onnx, or .engine), clean Python source code, and execution documentation.
Can the Computer Vision model run locally, on Edge devices, or in the Cloud?
Yes. Pipelines can be optimized for local execution (NVIDIA CUDA GPUs), lightweight edge hardware (Jetson, Raspberry Pi using ONNX/TensorRT), or packaged into Docker containers and deployed as REST APIs (FastAPI) on AWS or GCP.
How do you evaluate and guarantee model accuracy?
I evaluate all deep learning models using industry-standard quantitative metrics, including mAP (mean Average Precision), Precision, Recall, and Confusion Matrices. Target performance thresholds are defined during our initial technical scope discussion.
Can you optimize or fix bugs in an existing computer vision script?
Yes. I can debug existing PyTorch, TensorFlow, or OpenCV pipelines, optimize inference speed, resolve memory bottlenecks, or refactor standalone scripts into production-ready API microservices.
