TrustWatch Reviews and details

Questions & Answers

1. How would you describe your primary audience?

   Enthusiasts, Retailers and Repair Shops

2. What makes TrustWatch unique?

   The app uses your smartphone's camera to capture images of the watch, which are then analyzed by our proprietary AI model to determine authenticity.

   The type of machine learning being used is supervised learning, where the model is trained on labeled example images to learn to recognize and classify new images into the defined categories, thanks to the help of TensorFlow.

3. Why should a person choose TrustWatch over its competitors?

   The AI model is trained on a vast dataset comprising thousands of images of genuine and fake watches, covering a wide range of brands, models, and designs. This extensive training enables the model to learn and identify the unique features and characteristics of each watch, including the logo, dial, hands, markers, case, bezel, crown, and more.

   Without telling to much about the dataset, there is a "test" to check the accuracy of the model, the K-Fold Cross Validation, where it give the accuracy of the model, with a value from 0 to 1, where more near 1 is the value, the more accurate is it. In this case the Accuracy is 0,962 which means that is accurate for 96,20% of the time!

   However isn't a perfect system, this means that You need to double check the watch with other test and take to a professional store to have the final virdict about the authenticity of the watch.

   For new model or model that aren't inside the dataset yet, it can output a fake positive.

4. Which are the primary technologies used for building your product?

   The AI model is trained on a vast dataset comprising thousands of images of genuine and fake watches, covering a wide range of brands, models, and designs. This extensive training enables the model to learn and identify the unique features and characteristics of each watch, including the logo, dial, hands, markers, case, bezel, crown, and more.

   Without telling to much about the dataset, there is a "test" to check the accuracy of the model, the K-Fold Cross Validation, where it give the accuracy of the model, with a value from 0 to 1, where more near 1 is the value, the more accurate is it. In this case the Accuracy is 0,962 which means that is accurate for 96,20% of the time!

   However isn't a perfect system, this means that You need to double check the watch with other test and take to a professional store to have the final virdict about the authenticity of the watch.

   For new model or model that aren't inside the dataset yet, it can output a fake positive.