You will get a custom computer vision deep learning model with FastAPI web deployment
Project details
I design, train, and deploy custom computer vision models for industrial quality inspection, agricultural monitoring, inventory management, and commercial SaaS applications.
FULL-STACK COMPUTER VISION SOLUTIONS:
• Custom Model Training: High-accuracy image classification, object detection (YOLOv8/v11), and semantic segmentation (U-Net, DeepLabV3+).
• Evaluation & Optimization: Hyper-parameter tuning, evaluation metrics (F1-score, Precision, Recall, mAP), and confusion matrix validation.
• Production REST API: FastAPI service allowing client apps to send images and receive structured JSON prediction output in real time.
• Modern Web Applications: Drag-and-drop web interfaces built in Next.js for non-technical users to run vision models directly in a browser.
TECHNOLOGY STACK:
PyTorch, OpenCV, YOLO, FastAPI, Next.js, Docker, Python, Albumentations.
COMMERCIAL COMPLIANCE:
Built strictly for enterprise applications including automated quality control, industrial sorting, and commercial SaaS tools. Clean, fully documented source code is delivered upon completion.
FULL-STACK COMPUTER VISION SOLUTIONS:
• Custom Model Training: High-accuracy image classification, object detection (YOLOv8/v11), and semantic segmentation (U-Net, DeepLabV3+).
• Evaluation & Optimization: Hyper-parameter tuning, evaluation metrics (F1-score, Precision, Recall, mAP), and confusion matrix validation.
• Production REST API: FastAPI service allowing client apps to send images and receive structured JSON prediction output in real time.
• Modern Web Applications: Drag-and-drop web interfaces built in Next.js for non-technical users to run vision models directly in a browser.
TECHNOLOGY STACK:
PyTorch, OpenCV, YOLO, FastAPI, Next.js, Docker, Python, Albumentations.
COMMERCIAL COMPLIANCE:
Built strictly for enterprise applications including automated quality control, industrial sorting, and commercial SaaS tools. Clean, fully documented source code is delivered upon completion.
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, Google AutoML, Keras, NumPy, NVIDIA AI Platform, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, TensorFlow, Tesseract OCRWhat's included
| Service Tiers |
Starter
$150
|
Standard
$380
|
Advanced
$750
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 14 days |
Number of Revisions | 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
+$30
Additional Model Variation
(+ 1 Day)
+$150
Data Source Connectivity
(+ 1 Day)
+$100About Muhammad Hamza
Data Scientist & AI/ML Engineer | RAG Chatbots & Computer Vision
Gujranwala, Pakistan - 7:01 pm local time
I don't just build models in Jupyter notebooks. I deliver production-ready AI applications complete with FastAPI backends, Next.js user interfaces, and live cloud deployment.
CORE CAPABILITIES:
• RAG & LLMs: Custom document Q&A pipelines (LangChain, FAISS, Llama 3 via Groq), LoRA fine-tuning, and hybrid search.
• Computer Vision: Object detection, semantic segmentation (U-Net, SegFormer, SAM 2), image classification, and domain adaptation.
• Geospatial AI: Land cover mapping (LULC), satellite imagery analysis (Sentinel-2, Landsat), and change detection with foundation models (NASA/IBM Prithvi).
• Full-Stack Deployment: FastAPI REST APIs, Next.js interactive web dashboards, and Docker containerization.
PROVEN DELIVERABLES:
• AskDoc AI: Full-stack RAG web application for real-time document querying deployed live.
• GreenWatch: Interactive satellite change detection dashboard deployed on Vercel.
Whether you need an MVP for investors, an enterprise RAG system over your documents, or a custom computer vision web application; I build it end-to-end.
Let's discuss your project!
Steps for completing your project
After purchasing the project, send requirements so Muhammad Hamza can start the project.
Delivery time starts when Muhammad Hamza receives requirements from you.
Muhammad Hamza works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset Analysis & Annotation Verification
Analyze image dataset, verify annotations, establish baseline metrics, and format train/val/test data splits.
Deep Learning Model Training & Optimization
Train custom PyTorch or YOLO architecture, tune hyper-parameters, and evaluate confusion matrix and precision/recall.