Contact person: Branded Accessories Recognition AI

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Location: Delhi, India

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I need an end-to-end image-recognition solution that can reliably spot handbags and watches in photos and then identify the exact brand. The scope covers everything from assembling or augmenting a high-quality training set through to delivering a production-ready model and a lightweight inference script or API.

My priority is brand-level accuracy—simply detecting “a handbag” or “a watch” is not enough; the model must differentiate between specific labels (I will provide the brand list once the project begins). Speed also matters, so I am happy with approaches such as YOLOv5/8, EfficientDet, or a custom PyTorch or TensorFlow pipeline, as long as you can keep inference times low without sacrificing precision. If you prefer another framework that meets those targets, feel free to suggest it.

Please include in your proposal:
• The dataset strategy (e.g., sourcing, labelling, augmentation) you plan to follow.
• The architecture you believe will hit brand-level recognition goals.
• What I will receive at hand-off (model weights, code, installation notes, and a short demo notebook or REST endpoint).

Acceptance criteria: the model must reach brand-level accuracy of at least 90 % on a held-out validation set I supply, and inference must run in under 200 ms on a single modern GPU." (client-provided description)