thibaut-duguet

4 ms·

1. 1.▲
   by thibaut-duguet 6y ago

   This is a good idea and there are actually 2 objectives when one wants to clean its dataset: - you might want to optimize your time and correct as many errors as you can as fast as you can. Using several models will help you ion that case,
2. 2.▲
   by thibaut-duguet 6y ago

   There was indeed a manual review of the "potential errors" highlighted by our algorithm to determine is it was indeed an error in the data or if it was an error in the prediction. The 20% corresponds to the proportion of objects t
3. 3.▲
   by thibaut-duguet 6y ago

   The process is actually a bit complicated but let me explain it to you. Once you are on a dataset, click on the label that you want and use the slider at the top right corner of the page to switch modes (we call it smart detection). You sho
4. 4.▲
   by thibaut-duguet 6y ago

   I've added screenshots of errors in the blogpost so that you have an idea of the errors we spotted. Let me know what you think of them.
5. 5.▲
   by thibaut-duguet 6y ago

   Our platform is actually designed for enterprise companies, so we don't provide open access unfortunately.
6. 6.▲
   by thibaut-duguet 6y ago

   To answer your first question, we had both bounding boxes added and removed, and depending on the dataset, the main type of error was different (I'd say it was overall more objectifs that were forgotten, especially small objects). It w
7. 7.▲
   by thibaut-duguet 6y ago

   I'm a Product Manager at Deepomatic and I have been leading the study in question here. To detect the errors, we trained a model (with a different neural network architecture than the 6 listed in the post), and we then have a matching