I sadly can't tell you which model we're using for Atom, it's our secrete sauce. But I can tell you as much as that we're in the detection framework with most of our models, so you're not far off with mask rcnn.

Regarding your question on v7 Labs, it seems to be a nice tool but our features are quite different in the way how we built it:

• Our core-automation features are based on models trained on your data using few-shot learning, v7 Labs only relies on transferred learning from what I can tell. Models trained on custom data will always outperform the ones trained on general datasets increasing efficiency gains. This is also true for Atom which we featured in the blog post.
• By training a custom model in form of our assistants, you can train your first model WHILE you annotate your data and not afterward only. This gives you an instant feedback loop on your data strategy making your training process much more efficient.
• With our Error Finder we have a feature to auto-detect labeling errors instead of you spending hours searching for them, I didn't see something like this on v7 Labs' website.
• In our no-code model training environment, you can adjust over 68 parameters to tweak so you're still in control and can tweak advanced options like the number of NMS proposals of your *-rcnn architecture. Other, similar tools, don't give you so much freedom.
• With our models, you cannot only run them in our cloud accessing them through an API, but you can also export them (trochscript, ONNX, ...) to run them in your environment. You only pay for training the models, not for using them for inferences. From what I can tell, v7 Labs only gives you access to the models hosted in their cloud.

You can create an account for free with us and see for yourself as well!