
Show HN: Curated, Pre-trained ML Models for Transfer Learning - hsikka
https://modeldepot.io/
======
minimaxir
Two of my projects, textgenrnn
([https://github.com/minimaxir/textgenrnn](https://github.com/minimaxir/textgenrnn))
and reactionrnn
([https://github.com/minimaxir/reactionrnn](https://github.com/minimaxir/reactionrnn))
are among the pretrained models.

While this is allowed by the MIT License (and there is sufficient attribution
to the source repos), it might be helpful to more explicitly state that the
curated models are forks/modifications. These models also have a dependency on
the source packages, which I can't promise that it won't have breaking changes
if I do decide to update the package.

I do like the new accompanying examples for those projects, and it's good to
see the projects actually being used! :)

~~~
rm999
Hey, I just installed and played around with reactionRNN; I hate to be
negative (especially to people open sourcing models, kudos!), but your model
seems to perform quite poorly. It immediately failed my "easy" smell tests:
[https://i.imgur.com/FvfuZgy.png](https://i.imgur.com/FvfuZgy.png), and didn't
really work on most of my other tests: "This book sucks" is 0% angry, "I'm
going to go home and listen to emo music and cry" is 0% sad, "Check out this
hilarious youtube video" is only 26% haha. Your example "He was only 41." is
100% sad, but "He was only 42." is 0% sad. These aren't hand-picked, these are
literally things I just typed in. From what I can tell it usually gets
anything negative wrong, and usually picks "haha" for the positive ones.

Subjective performance is worse than your included examples. I've been
building models my whole career, and what I've learned is most people will
take claimed performance at face value until it burns them. It's beneficial to
no one if someone comes up with an idea based off your repo description,
builds it out, then finds it doesn't work adequately. My advice is to update
your examples and test cases, and keep finding ways to improve the model.

~~~
minimaxir
The result is the _reaction_ to a given text; it won't fully be the same as a
sentiment analysis, unfortunately.

Additionally, as I put in the README notes, keep in mind that the network is
trained on modern (2016-2017) language. As a result, inputting _rhetorical
/ironic_ statements will often yield love/wow responses and not sad/angry.
That type of systemic bias in text analysis is currently unsolved and there
isn't an easy way to account for it.

"I am so angry" and "I'm going to go home and listen to emo music and cry" are
phrases that would likely be posted on Facebook ironically, and therefore
classifying it tricky.

For the other examples, yes, that result might be overfitting on characters
and while setting up the model I had difficulty accounting for that while
still getting the model to converge.

I'll admit it's not a perfect model (it was a side project while I was
frustrated during a job hunt), but it's a great proof of concept.
Unfortunately, since Facebook crippled their /posts endpoint, I can't get more
data to improve the model...

------
rememberlenny
This is a great. Thank you for making this.

The aggregation of models like this isn't new, but simple layout of models
available is useful. I can imagine being able to filter by model origin
(OpenAI/Facebook/Google/etc), origin date (year), compared use case (why
should I use VGG vs RNN).

Overall, great for people who are and aren't deeply immersed in ML.

~~~
hsikka
Thanks! You hit the nail right on the head there, while aggregation is nothing
new, we're hoping that the notebooks and tutorials provide an entrypoint into
being able to get up and running with the model more quickly.

We're actually hoping to build ModelDepot into a place where anybody can share
(well documented) models, and be able to discover the right ones for their
needs.

~~~
RasputinsBro
Hey, I can't really see the website. The page is blank..

~~~
mikeshi42
Hey! Sorry the site isn't rendering properly :'( Can you let us know what
browser/OS version you're on?

~~~
RasputinsBro
Tor Browser.

The page just reads "You need to enable JavaScript to run this app."

~~~
ZeroCool2u
Honestly, laughed pretty hard at this comment.

But seriously, you're gonna need to whitelist the site and enable JS. Or just
use a normal non tor browser.

------
andreyk
Definitely a cool project. It's crazy there is no standard model zoo anywhere
yet. But, a few thoughts:

-No about page? Not clear at all without cleaning on sign up button that model submission/voting is a thing. And many more details I'd be curious to know seeing this for the first time.

-A few things, like colorization, loaded reallllyyy slowly

-No search/stronger filtering?

-Ideally various project should have weights in ONNX standard, and there should be tabs/selection of different platforms. You'd have five different entries for implementatations of ResNets in each of the major platforms? Quite silly.

-This seems like it should be an open source project , given that it is submission/open source driven

-Although this is aesthetically pleasing, at the end of the day it's just some links to cool Github repos... I feel like it'd make a lot more sense to create this as a CodaLab worksheet ([https://worksheets.codalab.org/worksheets/0x818930127c4d47de...](https://worksheets.codalab.org/worksheets/0x818930127c4d47de84c1ceaadf04d014/)) ; the platform there is just more mature for this sort of thing. Or make this a fork of that with the whole Zoo angle.

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daniellerommel
For those using Wolfram Language or Mathematica, there is an unreleased Neural
Net Repository that is worth checking out. A few examples:

Caffe:
[https://resources.wolframcloud.com/NeuralNetRepository/resou...](https://resources.wolframcloud.com/NeuralNetRepository/resources/Yahoo-
Open-NSFW-Model-V1)

Keras:
[https://resources.wolframcloud.com/NeuralNetRepository/resou...](https://resources.wolframcloud.com/NeuralNetRepository/resources/ResNet-50-Trained-
on-ImageNet-Competition-Data)

------
hendler
I like the looks of [https://openmined.org/](https://openmined.org/) for
decentralized sharing of data and models.

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partycoder
One day there will be a trained model called "software engineer" and we will
be out of a job.

The training data may come from github, extracting requirements from issues,
and mapping it to the code that closed the ticket.

~~~
mikeshi42
But then we can _finally_ spend _all_ of our time on HN!

But we really do think ML, even in the near future, will automate some of our
jobs away (and that's a good thing!), if you're interested in some of our
thoughts on that: [https://medium.com/modeldepot/we-previously-talked-about-
how...](https://medium.com/modeldepot/we-previously-talked-about-how-transfer-
learning-will-radically-change-ml-c4fed21a65a2)

~~~
MayeulC
I was about to answer roughly the same.

If someone could step up and "take my job", it would free me up for other
things. I would be grad to be able to rely on a good "software engineer" model
to build the next generation tooling.

All in all, more performance, more efficiency, improving our tools bottom-up,
going for the low hanging fruit first, to free us from the less interesting
tasks. Isn't it what we always do and always did?

If a task is repetitive or deterministic, I always feel like I could be
spending my time on something more interesting, not on the implementation
details. The problem is that I have to take care of it myself, if I want it to
be done properly (and that's true as well in other fields than software, of
course).

Disclaimer: still a student (although nearly graduated). I won't bite you for
having more experience than I do :) And the phrasing here might be a little
provocative on purpose, don't take it too hard please.

~~~
partycoder
I strongly disagree.

First, you assume that AI will remain less proficient than humans, and
therefore suitable only for grunt work. Artificial intelligence has many
advantages over us, and this is often overlooked.

\- How much time does it take to train one software engineer? decades worth of
time. Once you have one, what does it get another? about the same amount of
time and money. In contrast, once you train one AI based engineer, the time to
get the second one is about the time of serializing the state of one and
deploying it multiple times. You could recruit and discard AI engineers on
demand for whatever you need.

\- They won't sleep, take breaks for lunch or coffee, get distracted, waste
time talking about their car, vacations, wine tasting and other stupid things.
They will just work. 24 hours a day. No breaks.

\- They won't consume information at a rate of 200 to 1000 words per minute,
they won't be limited by their visual field, or how fast can they type. They
may not even communicate using words, just transfer thoughts directly among
them, or even entire trained neural ensembles. Plus, once they learn something
that may affect they collectively rather than individually.

\- They will be aggressively supervised, ranked. Then, since their generations
are shorter, this will cause them to continuously improve at a much faster
pace than humans.

If this happens, the need for having the same amount of human engineers will
be lower if not zero.

Some people may argue that the human brain has billions of neurons and
synaptic connections. Sure. But which portion of that is actively used in
developing software? remove motor functions, autonomous functions like
regulating heart rate, respiration and such and you are left with much less.

------
DOsinga
This is great, I like the inclusion of the notebooks, makes it easy to get
started. The download link for the Image Colorization model doesn't seem to
work for me though - it gives an Access Denied error.

~~~
mikeshi42
Hey! Sorry, slight typo on our end, the notebook should still work fine, but
the manual download didn't work for a bit.

It's fixed now though! Thanks for the heads up and I hope you've found it
useful! Happy to hear any other feedback as well :)

~~~
DOsinga
Yep, works now. Thanks!

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orliesaurus
Thanks for submitting! Can you tell me how this differs from something like
[https://algorithmia.com/](https://algorithmia.com/) ? I am curious to
understand the main differences between their approach and yours! Cheers :)

~~~
edraferi
Algorithmia is a FaaS platform with strong service discovery and a lot of AI
functions. Model Depot is a listing of open-source models.

You could find a network on Model Depot and deploy it on Algorithmia. Then you
(And others, if you like) can use the model via RPC using HTTP calls or one of
the Algorithmia client libraries.

------
nukenuke
Are there any good models for transfer learning with audio ?

~~~
hsikka
That is an awesome question, we haven't found any yet but are actively
looking! We'll let you know if we find some good ones :)

