In this article, I look at established as well as state of the art models for semantic segmentation

https://heartbeat.fritz.ai/a-2019-guide-to-semantic-segmentation-ca8242f5a7fc

I recently got my hands on the Raspberry Pi 4 and the Google Coral USB Accelerator and i decided to program a little fun game in order to get an idea of the performance of these devices. This is a video summary about it:

https://youtu.be/ruGk99s9Yhk

Technical details:

• The machine learning model used is a MobileNet SSD v2 trained on faces, which is publicly available.
• The video frames processed by the machine learning model have the dimension 720 x 480 pixels
• The frames per second during this game fluctuate between 20 and 30 fps
• The program is written in Python and besides the TFLite model, most of the program is achieved with OpenCV

What i learned:

• The combo of a Raspberry Pi 4 which now has USB 3 and the Google Coral USB Accelerator are a powerful and cheap setup and should definitely evaluated for projects that deal with machine learning inference on the edge.
• The Raspberry Pi 4 CPU gets pretty hot, it sometimes went up to 80 degrees celsius! Although the heat issue might be partly fixed with a Raspbian update, i highly recommend to get an active cooling solution like a fan, a heat sink only lowered temperatures by 3-5 degrees in my case.
• I prefer working with these two devices over the Google Coral Dev Board because the latter one runs Mendel OS which i find hard to work with, there is a lot of restrictions when you get started and i had a hard time to find solutions, even to achieve something as simple as a right click or open a visual explorer. Raspbian OS on the other hand has just so much more support because of its great community. I haven’t had my hands on the Jetson Nano from NVIDIA so i cannot make a comparison here.
• I tried to convert other objects into ping pong bats which can be recognized by the MobileNet SSD v2 (COCO), such as bananas but realised that objects need to be quite big or close to the camera in order to be recognized reliably by the relatively small image resolution. Smartphones and books worked quite well with that model but not as well as faces.

What do you think about these devices combined? Do you know of a better device or solution for machine learning inference on the edge?

In any case, I hope this helps you make decisions for your future projects 🙂 !

Paul

What’s the best approach in dealing with open set identifications such as face id and re-id. I know these problems are still unsolved and give low results without human aid. So mainly Im asking for ideas on how to handle this topic. For the sake of the argument lets focus on face recognition and lets say all images are of high quality. So storing or comparing the first few hundred picture we might have somewhat okay results if we use clustering or threshholding but when the numbers get bigger the false positive rates sky rockets. So is there a way to rerank or retrain everyonce in a while. Or at least differentiate from known and unknowns. Im lost at this step.

Hi all,

Machine learning in the industry is a lot more than just models and mathematics. We wrote a blog post describing the phases that a successful machine learning project goes through, what potential pitfalls there are and how to succeed in each phase.

If you have any feedback or questions let us know!

Blog post

Hey, we at deepset.ai have been working very hard on an open source framework for Transfer Learning in NLP. It’s called FARM and the core idea is that we separate out the language model component (takes tokens, returns vectors) from the prediction head part (uses these vectors to make meaningful predictions). We currently have support for Text Classification, NER, Language Model Finetuning and Question Answering. Feel free to direct questions and suggestions to me. If this seems like something that you’re interested in, leave us a star on github!

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https://github.com/deepset-ai/FARM

Hi,

this is Florian from edaviz and we are currently thinking about working on bamboolib.

Please check out the short preview video and let us know what you think:

https://youtu.be/yM-j5bY6cHw

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The main benefits of bamboolib will be:

• you can manipulate your pandas df via a user interface within your Jupyter Notebook
• you get immediate feedback on all your data transformations
• you can stop googling for pandas commands
• you can export the Python pandas code of your manipulations

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What is your opinion about the library?

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Thank you for your feedback,

Florian

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PS: if you want to get updates about bamboolib, join our mailing list which is linked on the github repo

https://github.com/tkrabel/bamboolib

There are a lot of ‘SOTA’ lists but they usually include a few NLP, CV, and RL results and call it a day. However, there are a ton of papers that beat the SOTA on a variety of silly stuff.

Is there an easy way to find a lot of SOTAs to try to beat the simplest ones for practice/fun?

Hi everyone,

It’s easy to find documentation on single or multi classification and single variable regression, but when it comes to multiple regressions, I find it much harder.

If one wants to predict multiple variables (that we suppose not independent), like the consumption of a set of articles (given historical data etc…). So for each article we have, we want to predict its consumption.

What would be the most common way to handle this? What is the actual preferred approach ? For basic ML, I’m used to XGBoost and LightGBM but those two are not supporting multiple variable prediction yet. Regarding DL, I believe this would ask a tremendous amount of data (the more article we have).

Are there any papers talking about this subject? Or any piece of work that would help?

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The training data can be seen as such (and we want to predict art1, art2 etc…) :