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Lobe – Deep Learning Made Simple

lobe.ai

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Re: Lobe – Deep Learning Made Simple

#21

Post by adammenges »

ozgooen wrote: post #15
ozgooen wrote: post #14

Earlier quoted context omitted.

Is Lobe only for image data? Would it work for inputs that are text files or similar?

I guess another question here is what are heuristics for how many images are necessary for different levels of functionality. The demos look pretty impressive, but I'm not sure how much went into them.

We've been surprised how little data folks have needed to use. If you look at the examples page you'll see in the lower right hand corner of the screen shot the number of examples they uploaded and trained on. Some examples, like the water tank, it's fine to some extent if it overfits on the training data, because the nest cam will only ever be pointed at the water tank, and it's worked in all situations and been robust for us with only ~500 examples. Other times folks are more interested in prototyping out an idea to see if it's possible on a wider scale, so a small dataset works well to prove out an idea.

Re: Lobe – Deep Learning Made Simple

#22

Post by pixelHD »

I have a couple of questions -

Do you have a team implementing most of the new state-of-the-art model architectures (given how fast new ones keep getting published)?

If so, I'm assuming you keep associating some types of model architectures to the type of data being input? I'm just curious how you'd pick a particular architecture.

On the other hand, AutoML comes to mind, but IMO, the biggest hurdle of AutoML, and its ilk is the massive computational infrastructure requirements.

But great job, it looks really good and seems pretty intuitive!

Re: Lobe – Deep Learning Made Simple

#26

Post by mbeissinger »

pixelHD wrote: post #22

I have a couple of questions - Do you have a team implementing most of the new state-of-the-art model architectures (given how fast new ones keep getting published)? If so, I'm assuming you keep associating some types of model architectures to the type of data being input? I'm just curious how you'd pick a particular architecture. On the other hand, AutoML comes to mind, but IMO, the biggest hurdle of AutoML, and its…

Thanks! One of the benefits of Lobe is that users who build models from scratch can publish and share to use in other documents, like a community model zoo. We do this for the current architectures internally, but the goal is for the community to help keep up with the firehose state-of-the-art in ML.

Something really interesting we have discussed for a future feature is being able to train a model using the data of which architectures end up working best for different data types so that Lobe can use AutoML to suggest better templates starting out, or on the fly while you are building the model.

Re: Lobe – Deep Learning Made Simple

#30

Post by mbeissinger »

milesokeefe wrote: post #18

Pretty excited about this, I've been on the edge of my seat waiting to hear back from Google after applying for the AutoML alpha but this looks even better, especially because it allows exporting the model which AutoML has not promised yet. Also AFAIK AutoML alpha initially only supports vision tasks while this allows nearly any input type.

Thanks! Yeah our approach is that diverse applications need to be able to go in and customize the models to be useful, at least for the next few years until the algorithms for AutoML get better and replace the engineers :P
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