Just started to use this fantastic and amazingly elegant tool.
The goal is to use a Raspberry Pi 4 as a speed limit sign recognizer. I did some model training sessions with 30 images for each class, only 2 classes for this test purpose. I used Google Streetview as the source for the images. My results here: https://studio.edgeimpulse.com/studio/31244
The recognition level appears to be very low, although the images used for training should be good enough. Any suggestions on how to improve the recognition level?
I cloned your project and was able to get 77% training accuracy and 21% testing accuracy with the following changes, although past this I’m not sure what else could be done to increase accuracy, perhaps @janjongboom could help:
I went to Dashboard -> Rebalance Dataset.
I moved items from the test set to the training set until there were 50 in the training set - 25 40s and 25 50s. This made the training-testing split closer to 80-20 than 70-30.
Training cycles: 100, learning rate: 0.1, score threshold: 0.65 - experimenting with these could help accuracy.
I selected the Unoptimized float32 model as the model version.
I would also recommend that you try making all of your bounding boxes the same size. Also, more data would definitely help.
@robhazes Quick tip: you can lower the score threshold on the NN Classifier page to see more detail (no need to retrain) - that will give you a quick insight in whether the model can find the object, but just with too low accuracy. E.g. here is an example:
I already got better results, just by moving the few test images to the train folder, so I guess I just have to gather more training data to get where I want. But this gives good insight in why some images get the low score they do.
