I will custom yolo object detection and computer vision
AI Engineer, Computer Vision , Deep Learning , Python and YOLO Expert
About this Gig
Need a custom Computer Vision solution for images, video, or real-time streams?
I build custom YOLO object detection and tracking systems using Python, OpenCV, PyTorch, and deep learningtailored to your dataset, hardware, and use case.
Services include:
Custom YOLO object detection & classification
Object tracking, counting, line crossing & heatmaps
Real-time video analytics for webcam, CCTV/RTSP, and recorded video
Image segmentation, OCR and license plate detection
OpenCV image processing and preprocessing
Model testing, optimization and fine-tuning
FastAPI/Flask APIs and Streamlit/Gradio interfaces
You receive clean source code, trained model weights, inference scripts, documentation, and setup instructions.
Please message me before ordering with your task, sample data, target classes, and desired output so I can confirm the scope.
APIs:
Other
Programming language:
Python
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SQL
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Colab
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MLflow
Tools:
Jupyter Notebook
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OpenCV
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MLflow
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CVAT
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Colab
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PyTorch
Frameworks:
Scikit-learn
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Keras
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PyTorch
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Panda
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What can you build with YOLO and OpenCV?
I build custom object detection, classification, tracking, counting and real-time video analytics systems for images, recorded videos, webcams and compatible CCTV/RTSP streams.
Can you train YOLO on my custom dataset?
Yes. I can preprocess your dataset, train/fine-tune a custom YOLO model, evaluate it, and provide the trained weights and inference code.
Can you build real-time object tracking?
Yes. I can combine YOLO with tracking methods such as ByteTrack or DeepSORT for detection, tracking, counting and line-crossing analytics.
Can you work with CCTV or RTSP streams?
Yes. I can develop compatible real-time video analytics solutions for supported CCTV/RTSP streams, webcams and recorded videos.
What do you need before starting?
Please provide your goal, sample images/videos, target classes, available dataset, expected output, and deployment environment.

