openpilot VS netron

Yes, but he spent several years in self-driving cars (https://comma.ai), which while interesting is also a space that a lot of players are in, so it's not the same as seeing him back to doing stuff that's a little more out there, especially as pertains to IP.

We have a product for sale: https://comma.ai

We raised $18.1M and have made $28M in lifetime revenue to date.

Where are you getting your narrative?

What about comma? https://comma.ai/ Seems like our old friend geohot built exactly what you want.

Positive HN discussion: https://news.ycombinator.com/item?id=36927971

The issue was that the front camera on the windshield couldn’t see under the hood. You misunderstand how easy it is to solve for depth and distance with AI without requiring stereo cameras. Read https://github.com/commaai/openpilot

Nice to have: I want to get a self-driving add-on that supports some cars better than others. Not a must but high up on my nice-to-have list.

EDIT: I probably should have added that I currently have the base model of the Kona the lowest model available, and I am looking for a similar thing in the ioniq 6, because my understanding is that it's fully compatible with the comma.ai device and therefore I am not planning on getting the better on board driving system, the Kona that I got unfortunately was not compatible with that device.

I wonder how bad this is compared to the competition. https://comma.ai allows you to add self-driving features to a large number of non-Tesla cars so, if we’re including physical firmware hacks as a threat vector, I’d bet tons of alternative cars (new enough Honda Odysseys, Toyota Siennas, etc: probably anything with adaptive cruise control and lane following) have the same sort of potential vulnerability.

Not sure, but from the first article from 4 years ago:

>Last month, we had 1,209 cars drive a little over 1,000,000 miles

Let's say they've had zero growth since then, so 48,000,000 conservatively?

Actually, from their website [1]:

>100+ million miles driven and 10k users.

[1]: https://comma.ai

In exploring open-source projects, I've come across several promising tools capable of managing deep-learning models for images. Significantly, tools such as NETRON provide visualization of neural networks, while SHAP can be used for evaluating the significance of outputs.

We need to find out the exact input and output tensor names. A tool like Netron makes this super easy. Open the original .tflite and/or the ONNX model in Netron and click the Model Properties button in the lower left corner.

How is this different from e.g Netron https://github.com/lutzroeder/netron

Netron (https://netron.app/) is the best and mostly used NN visualizer. Just save your model and then simply load it via netron to look its layers and weights. If you want a more complex visualization then you can also play with Zetane ( but its paid, also have a free version) engine.