thanks, @morgan. how allergic one is tough and don’t think I have the data to solve 
the skin color thing is super important and (i think) solvable. I’m glad you mentioned. if I can stomach it, I’ll run the images through OpenCV contours or convert to greyscale and then try retraining.
Does anyone have experience scraping google maps/google street view images and then doing deep learning to identify street signs? Any advice on where to start with this (either the scraping side or the deep learning model selection side) would be welcome.
i’ve not done it and only can offer general thoughts - i googled a bit and found this https://rrwen.github.io/google_streetview/ . there might be better with some more exploration
I have been playing about with training a Danbooru Multi Classifier using V2. So I can use it in colorising and semi super vised projects.
| epoch | train_loss | valid_loss | accuracy_multi | accuracy_multi | time |
|---|---|---|---|---|---|
| 0 | 0.016210 | 0.014924 | 0.995065 | 0.996054 | 6:10:34 |
| 1 | 0.015684 | 0.015714 | 0.995111 | 0.995989 | 6:05:17 |
accuracy mult threshold of 0.2 and 0.4, so I am gonna say my training is going well.
Hi,
I have created a superresolution mobile app based on fastai’s superres notebook.
I didn’t do any interesting deep learning work here (although I’d love to implement this zero-shot learning method in the future!), my main work was on the deployment and demo side: I created both a web-service and a mobile app that enables to take/upload a picture, enhance it, and compare the new vs old. Hopefully, this code can also be reused to demo many of the other cool projects that are shown here (style transfer, semantic segmentation, etc…)!
Unfortunately, the server seems to crash with heavy pictures, so I restricted it to pictures <2MB. If you have any idea how I could solve this issue, please let me know !
Nice results, maybe you could divide the image into 4 parts and process them individually ? combine them up after that 
Sounds like a great idea, thanks!
nice Molly! this was somewhere on my todo list.
I never quite got a non-fastai simpson version of ugatit working - using other people’s repo, my training.
I have written a 2 part blog series, part-1 and part-2 about how to build an end to end pipeline for an image segmentation problem.
It builds a solution for TGS Salt Identification Challenge competition in Kaggle.
By following the blog, you would be able to rank in the top 4% of the Kaggle LB. The blogs have step by step explanation of the key concepts like
DataBlocks,
Transformations,
Using unet_learner
Building custom unet architecture based on se_resnext50
Adding hypercolumns.
Adding custom TTA.
Doing K-Fold predictions.
Creating a Kaggle submission file.
At the end of the blog, I have also added a few tricks which you can try to improve the solution further.
One small suggestion, instead of converting the mask values from 255 to 1 in change_image, you can try using IntToFloatTensor(div_mask=255) in batch_tfms.
What will that do exactly? This is the first time I’ve heard of that IE would this help in a case of say 0,1,255?
By default the mask tensor gets divided by 1, but for binary segmentation we may want it to be divided by 255 to make the values in the tensor from 0 to 1.
I’m working on fastcook, a collection of useful recipes for our fastai2 driven workflow.
The idea is to provide examples on how to use the “not so standard” bits of the library, it should be helpful to all levels of users, while the focus is on DL, it’s not limited to it at all! For example, type dispatch is an example that will make your life better with any python project 
I’m slowly and steadily adding recipes as I’m working on other projects, most of the examples are in the vision domain since currently it’s my main area of study.
I would like to invite all fastai chefs to add new recipes to the cookbook, it’s all built with jupyter notebooks with nbdev so it should be really easy to add new stuff 
Thank you @lgvaz for sharing. It’s very well done: concise and informative. A couple of weeks ago, I started this thread: Fastai v2 Recipes (Tips and Tricks) - Wiki. Please feel free to add some of your recipes there if you feel like. I’m presently working on time series classification and forecasting, and I will try to add some recipes to your fastcook repo whenever I will have some time.
That’s amazing! I had no idea about this thread, really really useful, I already see items that could be added to fastcook from there!
I would really appreciate that, if have any questions on how to add stuff to fastcook please feel free to dm me 
Sure! I’ll add some of the quickier recipes there
I’m exploring the fastai2 library by writing about various NLP methods in a blog (thanks to fastpages!). The first post is simply finding a dataset in a language other than English (I ended up with Norwegian).
The plan is to test various NLP methods with this dataset, and maybe also MultiFiT at some point. I’m not aware of any pretrained models for MultiFiT (monolingual) in Norwegian, so this would involve training a language model from scratch (I have no idea how to yet). It would be fun to cooperate if anyone have similar interests!
My ambitious plans are destroyed by quarantine so far, so I have switched to the just adoption fastai2 for my old projects
I have implemented Metric learning HardNet in fastai2 here
