CVAT

Mentors: Student: tahamukhtar20

Efficient data annotation is paramount in various fields. However, current annotation tools often lack flexibility in terms of input customization, hindering users from maximizing their productivity. In CVAT, there are shortcuts but these are not customizable. This project aims to address this issue by introducing customizable keymaps, allowing users to change keyboard shortcuts according to their specific annotation workflows.

Mentors: Student: Vidit Agarwal

Problems faced while developing labelled data: 1. Annotations made by a single person can include their biases into the data and eventually into the model 2. There are subjective tasks at times, which might not have a clear answer associated with them 3. The size of the dataset might be huge enough for a few people to annotate To resolve this, a solution is to let multiple people annotate the data and then aggregate the annotations based on a consensus. Crowdsourcing has become an essential paradigm for efficiently labelling large datasets, especially in the era of data-driven ML solutions. However, the challenge arises in aggregating the labels from a diverse pool of annotators, each with varying reliability. This project aims to develop solutions to handle the above problems and integrate their solution into CVAT. It