Currently I'm developing an Artificial Intelligence or Machine Learning that focuses on Image Processing. It's more like a research and development. After having a discussion with external teams in choosing tools, i decided to train all the datasets in AutoML service from Google Cloud Platform.
Besides the r&d phase, all implementation that related to the machine learning needed for further like web development, so user able to scan it via web-based i guess. Or even API that connects the machine learning system with a mobile app. Let's settle my goals at first for this development.
Besides the r&d phase, all implementation that related to the machine learning needed for further like web development, so user able to scan it via web-based i guess. Or even API that connects the machine learning system with a mobile app. Let's settle my goals at first for this development.
Expectation
- The system able to detect the brand and the type of the bike, the machine learning itself detect a brand of a bike, or even type of the bike (release year too. it'll be awesome if its able. lol). Farther image capture would be recommended for this kind of prediction to ensure it achieves accurate prediction. (example -- left image).
- The system able to detect the quality of the parts. In this case. close-up photo-angle needed like right side images. It prevents the misleading label-detection. Besides that, it helps the condition of the part are seen enough clearly for the prediction such as the scratches, dents, etc.
- The system able to differentiate customized and original parts. Since we're going to value every single part of the bikes, original parts that still attached on the bikes will get better values than customized parts (in the market). So that's why it's quite important to be able detect those.
