I followed a simple tutorial to train a custom object detector.
I got my loss up to 0.6, however my issue is that the detected will classify other objects as what I've trained it with. For example in my case it classifies a dog as macarooni and cheese.
What am I doing wrong ?
I faced exactly the same issue, where the model "remembered" the previous objects. There is a new configuration in the config file that is was not implemented when the video was made.
Inside the ssd_mobilenet_v1_pet.config file you have to specify the path to the checkpoint where the training will start, so it will have all the weights from the previous training, this config is fine_tune_checkpoint, below that there is from_detection_checkpoint so it will use the specified checkpoint, after that there is load_all_detection_checkpoint_vars which is set to true by default, but must be false if you want the model to "forget" the objects that it was trained on.
The problem is that load_all_detection_checkpoint_vars will load and fix all the weights, including the ones in the final layers not just the lower layer ones, so it will remember the classification and detection from past objects and misclassify with the new ones, since your *.pbtxt has different classifications. If you set it to false it will load the data and learn new weights for the final layers based only on your training set.
