Matthias Heller

Very popular tools for data visualization in computer vision are: Voxel51, Lightly, Aquariumlearning, Scenebox, ScaleNucleus/SiaSearch, Tenyks, Aquariumlearning, Visual Layer, and many more

Data visualization is important since it helps to make hidden patterns in vast amounts of data visible and tangible. It can help to better understand the distribution, bias, and representation of one's data. Knowing that will help to better curate a high-quality training set and ultimately lead to better model performance. Furthermore, understanding what data one has is helpful to determine how to collect more data and find the best data generation and augmentation strategy.

For computer vision, there are some popular tools for data visualization available such as: 1) Voxel51 2) Lightly 3) Aquariumlearning 4) Alectio 5) Scale Nucleus 6) Foxglove 7) Tenyks Each tool has advantages and disadvantages. In order to pick the right tool for you it's crucial to look at your use case and it's specific needs and requirements

If the machine learning model is underperforming I would start by 1) looking at the training data, 2) fix the training data, and 3) retrain the model This is much easier and will most likely yield better results than to focus immediately on the model architecture itself. In the field of computer vision I can recommend the following software to do that: 1) Lightly 2) Aquariumlearning 3) Voxel51 4) Tenyks 5) Alectio 6) Visual Layer 7) Scale Nucleus 8) and many more

This is really a crucial step. It's important to not only remove exact duplicated but also nearby duplicates otherwise the dataset will still be biased. The more diverse the data the higher the dataset quality usually.

Missing values are a typical problem in machine learning. There are the following approaches to tackle them: 1) collect more data (the easiest) 2) data augmentation 3) add synthetic data (generate artificial data) The alternative to that is also to rebalance the dataset to represent better the few available values.