I am using tensorflow (object-detection) on my own dataset (drone recognition), also only 1 class named 'drone', after about 30000 steps trained, my result model can detect drone with very high accuracy, but I got a problem, I used ssd_inception_v2_coco model and its fine_tune_checkpoint on model zoo, right now sometimes in my real time detection, it detected human face as drone (very big different between 2 objects like that), I think because of the old checkpoint. How can I prevent the detection of some object that have big different with my drone object, like human, dog, cat... Or can someone describe for me what problem here? Sorry for my bad english
Even if you train an SSD for one class, it automatically creates another class called background. The background is trained using the regions of the training images that are not labeled as the desired classes (in your case, drone).
An easy way out is to add training samples that include images that have both drones and the things that you don't want to recognize as drones, in the same scene. Doing this and then increasing the number of epochs should improve the precision.
If you are doing an application where there are frequent occurences of some objects with drones, another possiblity is to actually train the network for those things too. This will increase your training workload, but improve the accuracy.
Some implementations of SSD have an option for hard negative mining of data, so that mistakes made during validation are specifically used with training. If you are familiar with the code, you might want to check if this is available.