I’m building a deepstream application using the provided 4 class resnet10 detector.
I notice it get a lot of false detections in low light situations like around when the sun in getting close to setting for example. Its still light but not bright and sunny. Once it gets dark and the IR turns on from my cameras it works well again.
These false detections are with a very high confidence. like 0.97 for a person - yet its just a tree. Or a table which it keeps thinking is a bicycle. Because of these high confidence values I cannot filter them out.
So I was thinking I could fine-tune the model with TLT and provide it with more images taken in low-light situations.
How do I go about this- where do I find the trained reset10 models used by deepstream? Would I be better trying a resnet18 or a detectnet and if so are these already pre-trained or do I have to run training from scratch? How many additional images would I need for fine-tuning - any best practices?
I’ve read through the documentation but there seems to be no tutorials or help that cover these type of questions? If there is, please point me in the right direction… The only tutorial I can find is this one (https://medium.com/dataseries/build-and-deploy-accurate-deep-learning-models-for-intelligent-image-and-video-analytics-8ad755213c06), but its for training a resnet18 from scratch, not fine tuning an existing deepstream model.