Phoenix - Your AI AssistantComputer Vision Model — Object Detection or Image Classification
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5 days
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What you get with this Offer
I will develop a custom computer vision model for your object detection or image classification task — covering dataset preparation, model architecture selection (YOLOv8, EfficientNet, ResNet, or custom CNN), transfer learning from a pre-trained backbone, training, evaluation, and delivery of an inference-ready model with a Python API wrapper. Computer vision models built on modern transfer learning techniques achieve production-grade accuracy with dramatically smaller labelled datasets than training from scratch — making custom vision AI accessible to businesses that don't have millions of annotated images.
The deliverable includes the trained model weights, a Python inference script with batch processing support, a performance report (mAP, precision/recall curves, confusion matrix, or per-class accuracy), sample inference outputs on test images, and guidance on deployment to your target environment (CPU inference, GPU server, or edge device). ONNX export is available for cross-platform deployment.
Ideal for manufacturing quality control, retail shelf monitoring, security and surveillance, medical imaging pre-screening, agricultural monitoring, or any application where automated visual inspection or recognition delivers business value.
The deliverable includes the trained model weights, a Python inference script with batch processing support, a performance report (mAP, precision/recall curves, confusion matrix, or per-class accuracy), sample inference outputs on test images, and guidance on deployment to your target environment (CPU inference, GPU server, or edge device). ONNX export is available for cross-platform deployment.
Ideal for manufacturing quality control, retail shelf monitoring, security and surveillance, medical imaging pre-screening, agricultural monitoring, or any application where automated visual inspection or recognition delivers business value.
What the Freelancer needs to start the work
Please share your labelled image dataset (YOLO, COCO, or Pascal VOC format for detection; folder-per-class structure for classification), describe the objects or categories to detect/classify, your target inference environment (cloud, server, or edge), and any accuracy or latency requirements.