I will build yolo object detection and vehicle counting in python
FullStack Developer AIPython Expert
About this Gig
Turn your images or video into a YOLO object detection system that detects, tracks and counts the objects you need.
I develop computer vision solutions in Python for vehicle counting, object tracking and custom object detection. My portfolio includes a traffic vehicle detection and counting project.
Services can include:
- A pretrained YOLO demo on your sample images or video
- Custom YOLO training on your labeled dataset
- Object tracking and line-crossing counts
- Precision, recall and mAP evaluation for trained models
- FastAPI integration and deployment support
I use Python, OpenCV and PyTorch. You receive source code and setup instructions for the agreed deliverables.
Basic covers a pretrained demo. Standard covers custom training; Premium adds API deployment. Dataset preparation, object classes and target hardware must be agreed before ordering.
Send a sample image or video, the objects to detect and your target device so I can confirm the right scope.
Programming language:
Python
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Colab
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NoSQL
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MLflow
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Amazon SageMaker
Frameworks:
Scikit-learn
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DeepPy
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SimpleCV
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Keras
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PyTorch
My Portfolio
FAQ
What kind of computer vision projects do you handle?
Object detection, image classification, image recognition, segmentation, face recognition, OCR, and object tracking — using YOLO, OpenCV, PyTorch, and TensorFlow. Message me with your use case and I'll confirm the best approach.
Do I need to provide my own dataset?
For a custom object detection model, yes — your images give the best real-world accuracy. If you don't have a dataset, I can guide you on collecting one or use public datasets for the Basic package.
What accuracy can I expect?
It depends on your data quality and the number of classes. On Standard and Premium I include an evaluation report (precision/recall/mAP) so you see exactly how the model performs before delivery.
Can you deploy the model, not just build it?
Yes. The Premium package includes deployment as a REST API and optional Docker or edge-device (Jetson/Raspberry Pi) setup.
