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English
$
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I will train and deploy custom yolo object detection models in real time
Computer Vision Engineer, YOLO and Real Time Deployment
Computer Vision Engineer specializing in YOLO object detection, real-time deployment, and dataset quality auditing.
I build pipelines in PyTorch, OpenCV, YOLO, and TensorRT, including multithreaded r...
About this Gig
I train YOLO detection models that hold up after you deploy them.
Most detection work looks strong in the report and degrades in production. The usual cause is a leaked train/test split: duplicate frames on both sides, or a collection artifact the model learns instead of your object. I check for this first, so the metrics I report are the metrics you get.
WHAT YOU RECEIVE
Trained model weights, ready to run
Full training and inference code, documented
Precision, recall, F1, mAP@50, mAP@50-95, confusion matrix
Per-class breakdown of strengths and weak points
Written summary of failure modes and how to fix them
DEPLOYMENT
Standard adds a FastAPI inference endpoint in Docker, an API your application can call.
Premium adds ONNX and TensorRT export with benchmarked latency and FPS on your target hardware, where most freelance CV work stops.
WHAT I NEED
Labeled images in YOLO, COCO, or Pascal VOC. Unlabeled is fine, I annotate too.
Message me your dataset size, class count, and target hardware and I will tell you which package fits.
Programming language:
Python
•
SQL
Tools:
Jupyter Notebook
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OpenCV
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PyTorch
•
Other
Frameworks:
Scikit-learn
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Keras
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PyTorch
•
Other
My Portfolio
FAQ
My data isn't labeled yet. Can you still help?
Yes. I annotate detection datasets as a separate service — bounding boxes or polygon masks, exported in YOLO, COCO, or VOC format. Message me with your image count and class list and I'll quote both jobs together.
What accuracy will the model reach?
I can't promise a number before seeing your data, and anyone who does is guessing. Send me 20-30 sample images and I'll give you a realistic estimate of achievable mAP and the main difficulties before you order.
Will this run on a Jetson or edge device?
Yes, with the Premium package. I export to ONNX and build a TensorRT engine targeted at your specific hardware, then benchmark actual latency and FPS so you know the real throughput before deployment rather than after.
What hardware do you train on?
An RTX 5080. Training runs that take other sellers days finish here in hours, which is why my delivery times are shorter than most gigs in this category.
What if the model doesn't perform well enough?
Revisions are included in every package. If the ceiling is your data rather than the training — too few examples, inconsistent labels, ambiguous classes — I'll tell you plainly what would need to change and what result to expect.
Do I get the source code and full ownership?
Yes on both, in every package. You receive the complete training and inference code, documented, and you own all deliverables outright. No licensing terms, no ongoing fees.
