In our quest to project completion, we had to work around the following challenges:
Data gathering, labeling & augmentation
There was initially insufficient data available to train our solution model. The data also needed to be diverse. So, we processed millions of images, using various open-source databases and trained our AI model on that.
We used data augmentation techniques to address the need for diversity by taking in account different backgrounds, lighting conditions, invariant poses, etc.