You will get yolo object detection and computer vision applications
Project details
I will train and evaluate a suitable PyTorch model for object detection or image classification. Depending on your task, this may include a YOLO detector, a pretrained or custom CNN, or a Vision Transformer (ViT).
The service can include dataset and label validation, preprocessing, train/validation/test configuration, model training or fine-tuning, package-specific model variations, and evaluation using appropriate metrics such as accuracy, precision, recall, F1, mAP and confusion matrices.
You will receive trained weights, configuration files, evaluation results, Python inference source code and documentation. A compatible ONNX or TensorRT export is included with Advanced or available as an add-on.
Base packages assume that your dataset is already labeled. Bounding-box annotation for up to 100 images and 1,000 objects is available as an add-on. Larger or unusual datasets require a custom quote.
Model performance cannot be guaranteed before inspecting the data. Cloud deployment, API integration, user interfaces, complete applications, segmentation masks and polygons are not included. Please contact me before ordering if your requirements fall outside the listed scope.
The service can include dataset and label validation, preprocessing, train/validation/test configuration, model training or fine-tuning, package-specific model variations, and evaluation using appropriate metrics such as accuracy, precision, recall, F1, mAP and confusion matrices.
You will receive trained weights, configuration files, evaluation results, Python inference source code and documentation. A compatible ONNX or TensorRT export is included with Advanced or available as an add-on.
Base packages assume that your dataset is already labeled. Bounding-box annotation for up to 100 images and 1,000 objects is available as an add-on. Larger or unusual datasets require a custom quote.
Model performance cannot be guaranteed before inspecting the data. Cloud deployment, API integration, user interfaces, complete applications, segmentation masks and polygons are not included. Please contact me before ordering if your requirements fall outside the listed scope.
Machine Learning Tools
NumPy, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, Python, Python Scikit-Learn, PyTorchWhat's included
| Service Tiers |
Starter
$100
|
Standard
$250
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 3 | 5 | 5 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$25
Additional Model Variation
(+ 3 Days)
+$75
Train on 500 More Images
(+ 2 Days)
+$50
Compatible Model Export
(+ 2 Days)
+$50
Annotation
(+ 3 Days)
+$75Frequently asked questions
About Fahad
Computer Vision Engineer | YOLO, Python & Data Annotation
Karachi, Pakistan - 5:50 pm local time
I can help with:
Bounding-box annotation for images and video frames
Annotation review, class consistency checks, and label quality assurance
Pascal VOC-to-YOLO conversion and dataset organization
Custom YOLO and YOLOX model training and fine-tuning
Image classification using CNNs and transfer learning
Train, validation, and test splitting with duplicate and leakage checks
Model evaluation using precision, recall, F1, mAP, and error analysis
Real-time Python inference, object tracking, and TensorRT optimization
Relevant hands-on work:
Built a two-class PCB solder-defect detection pipeline covering annotation conversion, controlled dataset splits, model training, threshold calibration, and frozen-test evaluation.
Prepared a four-class vision dataset and integrated a YOLO detector with ByteTrack and a Python control state machine for a gameplay simulation, reaching approximately 30 FPS with TensorRT FP16.
Built an EfficientNet-B0 defect classifier and used two-stage error analysis to determine that detector localization—not classification—was the main performance bottleneck.
You will receive:
Organized and reproducible Python code
A clearly structured dataset and documented setup
Honest metrics, limitations, and error analysis
Regular progress updates and a clean project handoff
I pay close attention to label quality, split integrity, and correct evaluation because those details determine whether a model works beyond its training data.
Send your dataset format, class list, annotation requirements, and expected deliverable. I will review the scope and explain what is realistically achievable.
Steps for completing your project
After purchasing the project, send requirements so Fahad can start the project.
Delivery time starts when Fahad receives requirements from you.
Fahad works on your project following the steps below.
Revisions may occur after the delivery date.
Scope and Dataset Review
I will review the task, dataset structure, labels, classes, target hardware and expected deliverables. Any blocking data or scope issues will be identified before training begins.
Data Preparation
I will validate the annotations, prepare reproducible dataset splits and configure preprocessing. If purchased, the agreed bounding-box annotation add-on will also be completed and checked.