
Lobe – Deep Learning Made Simple - adammenges
https://lobe.ai
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pixelHD
This reminds me of Unreal's blueprints. You could get a surprising amount of
work done just via blueprints and not touch C++ code.

You'd have to balance the input and output connection's granularity - too many
would put off users, too few would make users feel restricted. If you manage
to find a sweet spot, or let the user pick the level of expertise, and reveal
them accordingly, it would be perfect.

I really like how good it looks, and can't wait to use it myself!

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jonnydubowsky
Great description of the balance of features between super-users and beginners
interested in frictionless AI tooling based on their level of experience. I've
used the example of the settings, preferences and pre-built sketches within
the Processing, P5js and Arduino IDE's to imagine what an AI model manager
might look like if it followed this tone. Do you have any other examples of
products that meet the requirements of both beginners and super-users?

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seltzered_
Is there any backstory of how this came together?

I just watched the video, and one of the first thoughts was mike matas’s 2016
video “the brain” where he made an NN within quartz composer:
[https://youtu.be/eUEr4P_RWDA](https://youtu.be/eUEr4P_RWDA)

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mbeissinger
Yeah! Adam and I met at NIPS 2015 when I demoed a gui prototype for OpenDeep,
talking about ways to let non-coders build neural nets. Around the same time,
we saw that QC Brain video Mike posted and we all started talking and unifying
around this vision of helping people who aren't experts get started with
designing and adding intelligence to apps. From there we formed the vision for
Lobe and the rest is history!

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k1ns
This is really cool. What will the pricing model be down the road? For
instance, if I were to use these models and the cloud API to service a main
feature of my application, what ballpark are you thinking of in terms of
monthly cost?

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mbeissinger
Our initial thoughts for pricing model are to be based on compute and keep as
low as possible - we want this to be accessible to as many people as possible
and do things like enable local training with Tensorflow.js for free. For
cloud API deployment, we will price around the backend compute cost on AWS/GCP
for gpu instances plus some margin for us maintaining the distributed setup
and scaling of serving a machine learning model.

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Taurus80
Grow it into a platform and have a marketplace. So people can train a good
building block / application (like image classifier / detector with the proper
preprocess steps) then the trainer/developer can sell it and others can use
it! Or people will just export out their models and go to marketplace ( like
[https://algorithmia.com/](https://algorithmia.com/))

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mbeissinger
Yep growing as a platform is the vision! We are committed to accessibility for
people using models/components vs. paying but good point about considering the
opportunity cost of someone going to algorithmia with a model once it is
trained.

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technologia
Wish there was a small demo to try but it looks great; It kind of reminds me
of the same type of sandbox/experimentation environment you get with
Tensorflow Playground (albeit not constrained to a few sample sets of data). I
look forward to seeing how this product turns out.

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mbeissinger
Thanks! We want to integrate Tensorflow.js to have the models run in the
browser for easy sandbox/experimentation. We loved the visual aspect of
Tensorflow Playground and feel a visual graph approach where you can see the
output of operations really helps you understand what is happening.

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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!

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mbeissinger
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.

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pixelHD
Oh gotcha! Didn't consider that you'd allow users to write their own models
too.

Coming to community model zoo, would it be free access to any model, and pay
for the training and disk usage (floydhub like)? Or you'd go the quantopian
route?

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mbeissinger
We are focusing on free to access any model explicitly shared by the user and
pay for training/deploy resources as a service, but might consider mixing in a
paid route for users to monetize their unique trained models.

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ibdf
First time I read Deep Learning and simple on the same sentence and it's
actually simple. Looking forward to use it.

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adammenges
This makes us really happy to hear! Thank you!

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imranq
I personally really like the design and looks super approachable. Can’t wait
to try it out!

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adammenges
Thanks! Super excited to see what you build :)

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phildionne
I'm blown away by the effectiveness of the whole product. While being
approachable for beginners, it seems to allow experts to tweak at will. It's a
massive "tour de force".

The marketplace play mentioned in other comments seems like a thing to try. I
confirm I would pay for querying predictions through the API.

Such a polished product for 3 folks. Kudos!

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deepGem
Awesomely done ! What i really like is that, in addition to the ease of
deploying a model, this product also lets you visualize the activations of the
neural network. I mean you could build the visualizations in Jupyter but the
ease of toggling the layers, such as switching of Max-Pool etc is super
helpful for understanding how neural nets work.

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adammenges
Yeah we definitely agree! Thanks for the kind words.

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milesokeefe
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.

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mbeissinger
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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mbeissinger
Hey everyone! One of the cofounders of Lobe here - let us know if you have any
questions.

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

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ozgooen
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.

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adammenges
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.

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RankingMember
This looks awesome. I liked the demonstration of real-world applications with
the water tank.

From a deep-learning novice: Can you give a rough idea of the processing cost
of doing something like setting up your water tank level recognition?

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mbeissinger
Thanks! We used the water tank as the first end-to-end test using the product,
and made a website that calls the API every minute to monitor the water level
in a dashboard.

The architecture implemented using Lobes for object detection is called Yolo
v2 ([https://pjreddie.com/darknet/yolo/](https://pjreddie.com/darknet/yolo/)).
It is fairly state-of-the-art for that type of problem and has ~70 million
parameters that are being learned (matrices that get multiplied and added
together). With a webcam and a GPU over the network, we typically see ~1-5 fps
with a lot of network overhead sending output images - looking to make that
faster for API deployment. The paper site above shows it having 62.94 Bn FLOPS

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Taurus80
Looks really impressive! Acumos plan to do something similar?
[https://marketplace.acumos.org/#/home](https://marketplace.acumos.org/#/home)

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mbeissinger
Thanks! Acumos has a similar vision of making it easy to build, share, and
deploy AI. We are also heavily focused on the iteration and feedback loop time
when developing AI applications, and importantly on helping users understand
what is going on with the model. That way you can know the limits of the
system in production, or know how/where to improve the data and help reduce
bias.

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jamesmcintyre
This looks AMAZING! I love the UX approach to balancing simplicity and
complexity. For instance, giving the granular control over the hyper-
parameters in a visual way with immediate feedback is a whole level above any
other design I've seen out there that tries to make deep learning more
accessible.

Awesome work and I hope to see the product grow!

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mbeissinger
Thanks! Finding the right way and levels to present granularity was definitely
a challenge for us developing this product.

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ghosthamlet
I think there are something like this, don't know what the difference, can
someone give a compare:

deep learning studio: [http://deepcognition.ai/](http://deepcognition.ai/)

knime: [https://www.knime.com/](https://www.knime.com/) (have deep learning
plugin)

runwayml: [https://runwayml.com/](https://runwayml.com/) (in beta, did not
open for public)

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rpedela
knime and deep learning studio look like tools for ML experts and I am not
sure what runwayml does. Lobe appears to be something that a software engineer
could use but they don't necessarily need to be a ML expert. We will see if
Lobe delivers on that promise, but I think that is the difference.

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bayonetz
Knime is very similar concept giving you access to building blocks like
models, evaluators, input transformers, etc. so that you can make arbitrary ML
pipelines. It doesn't have the slick drag'n'drop of input data. Instead you
use an input building block and point it to a data store of some sort (text
files, images, database, etc.). Also, it doesn't generally output models for
direct use in other applications but you can output using PMML. Knime is also
very useful for analysts, data scientists, etc. who aren't software engineers.
Lobe has run with the concept pioneered earlier by folks like Knime,
RapidMiner, WEKA, etc. They've simplified the process of quickly getting a
working model by constraining on one model type and one input type. If your
use case matches, it's a great innovation. If not, per usual, no free lunch.

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real-hacker
Great, great work! Congratulations!

Do you support transfer-learning, for instance, pre-trained models on
ImageNet? A lot of problems have limited dataset, and can only work by
training the last layers of a pre-trained model?

And do you support training on cloud-based public datasets? Uploading a large
public dataset doesn't make much sense.

Really looking forward to trying your platform!

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ddtaylor
Looks great keep up the awesome work!

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adammenges
Thanks!

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jd20
Very cool, any plans to bring it to desktop as well? Seems like this could be
a very useful tool for people getting into deep learning, but may not
necessarily be targeting mobile. (From the landing page, it seems mobile-
only.)

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mbeissinger
Yes definitely, we support desktop currently with the Tensorflow SavedModel
download if you need the model locally, or using the REST API for an
application with online connectivity.

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ghosthamlet
Are there some tools like this but for deep/machine learning
internals/theorys/math study/research rather then practical deep learning ?

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mbeissinger
Our vision is to be the tool that starts with great settings for beginners but
lets you graduate into the internals as you become more expert - at the lowest
level you can interactively create computation graphs and see their results as
you change settings, sort of like eager mode for ml frameworks on steroids (or
other visual computation graph programs that designers use like Origami/Quartz
Composer).

The lobes in the UI are all essentially functions that you double click into
to see the graph they use, all the way down to the theory/math.

If you want more comprehensive ways to learn the theory, I highly recommend
Stanford's 231n course
([http://cs231n.stanford.edu/](http://cs231n.stanford.edu/)) and the
Goodfellow/Bengio/Courville Deep Learning book ([https://www.amazon.com/Deep-
Learning-Adaptive-Computation-Ma...](https://www.amazon.com/Deep-Learning-
Adaptive-Computation-Machine/dp/0262035618))

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ghosthamlet
@mbeissinger thanks, great to see Lobe have low levels for play.

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_bxg1
Given how polished it is I expect this isn't a free service, but I can't see
any info about pricing/how payment works

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mbeissinger
We are currently accepting applications for private beta users, and don't have
public pricing information yet.

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manutoky
Is this a standalone application or a web service?

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mbeissinger
Web service with the option to download a compiled Tensorflow SavedModel or
CoreML file if you need the model locally.

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KaoruAoiShiho
Is this a good way to build a board game AI? If I have a db 50,000 replays
would this be the right tool to build it?

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mbeissinger
We are working on supporting time series data, so coming soon.

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smel
Great job ! any information about pricing ?

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mbeissinger
We are working with private beta users now, will have pricing up when it is
public.

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ankit84
Impressive!

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adammenges
Thanks!

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kbcool
TL;DR - GUI for building Bayesian classifiers?!

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mbeissinger
Haha well a GUI for building most computation graphs if you want - at the core
you can get down to doing most TensorFlow operations. We even built a GAN
using it!

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langitbiru
So IDE for Tensorflow. Nice!

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manuaero
great design!

