The results highly depend on how well trained it is on sample images. You need thousands and thousands of images to properly train it.
If it has enough of the exact category you are looking for, you should get pretty accurate results, but you need a lot of samples that are not the category as well or it will think everything is the category you are looking for.
You are using a pre-built model. But if you want to look for things they don't cover you can make your own model with your own sample images.
Even then though, a lot of the times you start with their trained data and retrain it for more specific cases you will be looking for and this is where you save a ton of time and get really good results.
For example, the data set you are using has a lot of cats, dogs, animals, vehicles and some things in the real world. Let's say you want to change it to detect GPUs based on pictures. You can take the existing trained model and remove the last few layers that branch out to the categories and do additional transfer learning from there, this will allow you to make new categories and add to what it knows already.
Would be a good learning experience to train that model to look for categories, not in the original data set. For example, detecting the difference between FedEX and UPS trucks. The model is already trained well in detecting trucks. So it would be fairly easy to get it to detect specific company trucks.
But whatever you do, make sure you use notebooks, it will make your life sooo much easier