Just built my version of the face-mask detector after completing lesson 2, with streamlit.
mask-ornot.herokuapp.com
and here’s the repo.
Found all the help needed here in this forum.
Another perspective of learning fastai course
I have been trying to do binary segmentation, for hair from peoples faces, and think its been doing quite well at the moment. I even combined it with a color LUT to do some nice recoloring of hair. !
@muellerzr this thread should be moved to 2020 i assume?
2020 has it’s own
Survival Analysis with pytorch using nbdev. At the end of the day it’s just another loss function. Made the docs so that (hopefully) you can understand the theory as well as code.
Still some work to be done in the Accelerated Failure Time models, but here’s the alpha: https://sachinruk.github.io/torchlife/
I used UNET architecture for generators in Cycle GAN. I observed a significant improvement.
Here is my work
I wrote a quick article on how to use Streamlit to quickly test your models. This can be used for fastai models as well. Link below
https://towardsdatascience.com/streamlit-use-data-apps-to-better-test-your-model-4a14dad235f5
Hi dipam7 hope all is well!
Great short post"
not registered with medium_com
Cheers mrfabulous1
Hi,
I have written a blog and developed a Covid19 chest X-ray image classifier based on the initial 3 lessons. Here are the links:
https://www.kaggle.com/krrai77/fast-ai-lesson-3-covid19-x-ray-classifier-app
Thanks
Raji
I’ve figured out that we don’t have to do any fine-tuning but just correct the weights in the bigger kernel with respect to how much bigger it got.
5x5->9x9; have to multiply the weights by (5x5)/(9x9).
Nice mind map ! I like it
Hi everyone! Want to introduce the Weights & Biases report about transformers, which dives into the breakthroughs, scientific basis, formulas, and code for the transformer architecture, which you can read in the Chinese.
Report: https://lnkd.in/eVi74B9
Our paper using ULMFit to generate potential inhibitors of SARS-CoV-2 Mpro is finally here!
Recognising MNIST digits using mean of pixel values, without any machine learning algorithm
I have created a well documented python code for classifying digits without machine learning. Will be happy to hear the suggestions for improving the code.
I can reached at: ankbassi100@gmail.com
How to Deploy Fast.ai Models? (Voilà , Binder and Heroku)
Medium article:
code:
I hope this may help.
Car Type Classification
Classifies 4 types of cars.
- Convertible
- Pickup
- Sedan
- Station Wagon
In this article, I use a fun little example of how pytorch-lightning started to demonstrate the power of GitHub Actions
https://dipam44.medium.com/the-simplest-introduction-github-actions-43e86b05317
I was a bit fuzzy about python decorators, and since they’re used everywhere in fastai I decided to dig in and wrote this post, which is part 1 of 2 parts.
The next post is going to be about dissecting the fastai function module
which had me confused for quite a bit before I started to break it down!
feedback welcome.
Hope all is well!
Nice Work!
Cheers mrfabulous1