You will get a custom object detection and tracking system on your videos
Project details
Computer Vision Engineer with hands-on experience in satellite imagery, real-time detection, and surgical tracking. I deliver custom YOLO models trained from scratch, semantic segmentation pipelines, and optimized inference via ONNX. From dataset to deployment, I handle the full pipeline: training, evaluation, tracking, API, and Docker.
AI Development Type
Deep Learning, Knowledge Representation, Model TuningAI Tools
MLflow, NVIDIA AI Platform, OpenCV, PyTorch, Sonnet, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$50
|
Standard
$100
|
Advanced
$250
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 9 days |
Number of Revisions | 2 | 3 | 5 |
AI Model Integration | - | - | |
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$25 - $125Frequently asked questions
About Ahmed
Computer Vision Engineer | Object Detection, Segmentation & Tracking
Villeurbanne, France - 12:16 pm local time
Steps for completing your project
After purchasing the project, send requirements so Ahmed can start the project.
Delivery time starts when Ahmed receives requirements from you.
Ahmed works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements, dataset, and define project scope
Analyze your dataset, understand your use case, and define clear objectives. We agree on target classes, expected accuracy, output format, and timeline before any work begins.
Data preparation and augmentation pipeline setup
Clean, format, and split your dataset. Apply augmentations (flips, rotations, mosaic, color jitter) to maximize model robustness. Convert annotations to the required format.