ZBzbyte64·Nov 15, 2017
First time I heard of DRAGNN, my mind is blown: https://github.com/tensorflow/models/blob/master/research/sy...
In SLING: A Natural Language Frame Semantic Parsertensorflow
models
Python2016–2022
tensorflow / models
models
Models and examples built with TensorFlow
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65Mentions
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Models and examples built with TensorFlow
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Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 7:02:50 PM UTC.
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HN mentions by year65 dated mentions
Observed mentions: 2016-03-09 20:36:16 UTC to 2022-07-12 14:14:04 UTC.
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View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 16 | 4 | 12 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 25 | 2 | 23 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 12 | 4 | 8 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 5 | 0 | 5 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 3 | 2 | 1 |
What Hacker News says
25 sourcesSo the object detection extension is mostly focused on breaking down complex operations like anchor generation into functional steps that are compatible with the base tensorflow metagraph definition. You can see these builders here: https://github.com/tensorflow/models/tree/master/research/ob...
What that means is that when it comes to inference in a production environment, we only need our Tensorflow python package, as the metagraph is defined in terms that the base Tensorflow package can decypher.
Although as I'm not an author of the object detection API, there is probably a more nuanced answer here.
In Object Detection with Open Images using Tensorflow+1
simple offline app and this could be the digital document safe 2017 deserves. no big data player, German/European servers and law behind it.
some stuff I enjoy while using similar services:
- from photo to pdf
- auto tag (maybe Textsum: https://github.com/tensorflow/models/tree/master/textsum)
In Show HN: DocumentDeck – Simple Document Management for IndividualsTL;DR; tooling is improving rapidly, and while this AI boom will someday end, we'll be left with tools to incorporate the things that do work in our everyday work.
Working for a "digital innovation lab" where a big part of my job is sorting out things that developers can actually use to make cool products from Gartner report buzzword hell, I have a few thoughts about this.
Historically, AI has seen boom and bust cycles. AI, unlike other buzzword technologies, has a large cultural mythos about it - Asimov didn't write "I, EC2 Instance", after all. Thus, non-technical people have very strong and often unrealistic set of expectations around AI, so every time there are technological advances, people expect that general AI is right around the corner, and are disappointed when it doesn't materialize. Right now, we're experiencing a boom cycl
Read the rest
e as a result of the emergence of deep learning techniques. After every boom, funding dries up and investors loose interest, but the techniques that really work stick around and become part of a developers everyday toolkit.
Like all "black sorcery" technologies, AI has a ways to go in terms of building convenient tooling. Tensorflow is a huge improvement over writing low level CUDA code, but it's still too low level for folks without a strong background in mathematics and machine learning. That said, it's been improving in usability, documentation, and tooling, and just a couple of weeks I was part of a hackathon where we turned their pet recognition demo[1] into software to detect objects of interest to my company in satellite images.
At an even higher level are a number of startups (such as Clarifai[2]) that offer AI-as-a-service. In Clarifai's case, you can train your own image recognition models and apply them with a few lines in your favorite programming language (yeah, yeah, I'm shilling them a bit, but I really like their product ;-).
So at the end of the day, I think we'll all be building various kinds of AI into our products in the not-so-distant future, but you won't really need to go deep into tensorflow and similarly low level tools to do so.
[1] https://github.com/tensorflow/models/blob/master/object_dete...
[2] https://clarifai.com/developer/quick-start
How many languages are supported? I see many more languages in Google's Syntaxnet library. What's keeping you from having the same list of 40 languages for POS tagging?
https://github.com/tensorflow/models/blob/master/syntaxnet/g...
In Prodigy: A new tool for radically efficient machine teachingSpacy is an option; for syntactic parsing, you might also want to take a look at Stanford CoreNLP (https://stanfordnlp.github.io/CoreNLP/) or NLTK (http://www.nltk.org/) or SyntaxNet (https://github.com/tensorflow/models/tree/master/syntaxnet).
In The ImageNet dataset transformed AI researchSequence-to-sequence attention models: https://github.com/tensorflow/models/tree/master/textsum
https://arxiv.org/abs/1509.00685
In Ask HN: How far are we with abstractive text summarization?Google's Attention OCR in tensorflow:
https://github.com/tensorflow/models/tree/master/attention_o...
In Using Tesseract OCR with PythonTensorflow has the solution to all problems :)
https://github.com/tensorflow/models/tree/master/slim
This is a flexible image recognition framework written in Tensorflow and TFslim
It allows you to train/fine-tune a NN from the command line by specifying only the few details necessary... which NN architecture to use, path to custom data, etc.
In Sony open-sources NNabla – a simple, fast and lightweight NN libraryHello author of the article here!
I will probably publish in some time (when I'm not so busy doing other projects) some code samples too but until that I can give you tips.
You should start inspecting how the pre trained models (Inception v3/v4) works, what kind of layers they have and then decide what layers you want to use and what not. In case of Tensorflow, the tensorboard is very good tool for inspecting the model inner layers.
If you want to get started even more easier you should probably take a look into Tensorflow Slim models (https://github.com/tensorflow/models/tree/master/slim)
There is quite beginner friendly instructions for simple fine tuning of the models and it should take you pretty far.
In Using Tensorflow to Recognize Saimaa Ringed Seals from WebcamYes, if you train those models using a dataset with box annotations. A more relevant model if you want to transcribe the text : https://github.com/tensorflow/models/tree/master/attention_o...
In Supercharge your Computer Vision models with the TensorFlow Object Detection APIHoly moly, I can't believe I didn't know about https://github.com/tensorflow/models
In Supercharge your Computer Vision models with the TensorFlow Object Detection APIClicking through the two layers of links, it is a GitHub repository containing pre-trained models, training scripts, and scripts for running the models on Google Cloud: https://github.com/tensorflow/models/tree/master/object_dete...
In Supercharge your Computer Vision models with the TensorFlow Object Detection APIThey have pretrained weights from the COCO dataset included with the open source models.
https://github.com/tensorflow/models/tree/master/object_dete...
In Supercharge your Computer Vision models with the TensorFlow Object Detection APIIt has been added to the TensorFlow github repository like Inception. You can use it completely independently from Google.
https://github.com/tensorflow/models/tree/master/object_dete...
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