This general idea (substitute whatever appropriate ML technique for CNN’s) has been part of the lore of signal processing and machine learning for a very long time. I remember people doing this kind of thing to the time indexed frames or windowed segments of the short term Fourier transform. I’m sure it was done a long time before the STFT became the standard time-frequency analysis transform.
I have seen this done in time frequency masking (see Yılmaz and Rickard, 2004). Image processing techniques have been applied to the time indexed frames of the STFT or the binary mask function that “picks” the STFT points that belong to a specific audio source. I wish I had some examples, but in my experience applying image processing techniques was a pretty ad hoc thing to do although it often yielded good results.
In my experience this translates to the necessity of meaningful feature selection.
This app was developed by me who had no coding background and me and my High school friends have developed this. Thank you Jeremy, you are a true inspiration.
Hi all! Just finished lesson 1 and decided to have a little fun gathering my own dataset for this week’s project: identifying whether a person is wearing their mask properly or improperly. Images were manually gathered using Google Images. A total of 45 images of proper mask wear and 45 images of improper mask wear were gathered. Here is a random batch:
I managed to reach an error rate of 17% (3 out of 18 images incorrectly classified). Not too sure how to improve this given the small dataset size. I tried applying some data augmentation using mixup, but it did not help much. Here are the top losses (some very obvious errors):
Ha Ha LOL! Hi stochastic your post made me laugh, I hope your having lots of laughter today to!
Try adding some more pictures to your classes and try it with a bigger model. See this post here.
first, i have to say, i’ve been writing code for a little over 30 years so i’ve seen my fair share of really good and really not good, and this library is absurdly good.
i gave it some horses and donkeys to play with. it took quite a long time to run lr_find() but after that i got 93% accuracy, training in about 10 seconds on colab free.
i also went on a little tangent because i couldn’t use the javascript method from lesson 2 to get the urls out of google (i’m running on a tablet for the next 2 months) so i created a notebook to do the image scraping/cleaning/zipping.
I finished lesson 3 yesterday and been playing with what I learned so far. Specifically, I took a Kaggle dataset for pneumonia chest x-rays and built a classifier. I’m went through the usual process for training, playing around with learning rates after unfreeze to see if I can improve the result. I ended up with 93.5%.
On the Kaggle forum, people are getting around 98%. I’m wondering what I can do to improve my results?
Should I keep fine-tuning the learning rate? Would it help to expand the validation set to be a larger percentage of the overall data? Is there something I can do with test data to improve the results?
I’ve finally been able to finish the Lesson 2 webapp with the help of mrfabulous1. It’s a coin time-period identifier. However, the model is very biased since I pulled most of my images from a UK finds database. I did try to compensate a little by adding some images from other sources, but the data is still going to have trouble with most coins minted outside the UK.
Edit: I should mention that my accuracy for my model ended up being something like 88%-89%, mostly due to similar motifs showing up between the medieval-ish time periods. That, and some coins look like they’re incredibly difficult to id unless you have the expertise to do so.
Also I hadn’t realised that Colab was giving out my email address when people run my notebook through a sharing link(!) so I’ve put that image scraper into github instead.
As of 07/15/20, I was able to use these instructions for jetson-nano but with several adjustments. Installed JetPack 4.4, torch 1.4.0, torchvision 0.5.0. Use instructions from nvidia for torch & torchvision link.
Yes, I killed that link when I realised it was telling people my email address when they ran it. It’s on GitHub now, and there’s a little tiny button right at the top of it you can click to load it up on Colab. Here.
I’ve just released an update to fastinference, and two new libraries:
fastinference_pytorch and fastinference_onnx
The goal of these two is to be lightweight modules to run your fastai models with in a familiar API. Currently it just supports tabular models but vision and NLP are on the way! See below for a numpy example (there is zero fastai code being used )
I learned how to write a simple transform (which honestly didn’t have to be a transform, but I did it for the knowledge), so they could look like this:
Then put them through a custom CNN where I wrote all the layers in Pytorch’s api, then in fastai’s api, so I could learn everything in between. Eventually, I created a model that gave me about 97% accuracy (there we sampling issues, so take that with a grain of salt). And, as a final exercise, I recreated a heatmap the way Jeremy did with his cat, so I could visualize the outputs of my convolution. They looked something like this:
Learned some things about fiddling with my batch size, handling tensor shapes, watching for GPU usage, etc. Was good fun, albeit all my training attempts were a little tiring.
Please, I would like some guidelines, I am trying to understand how i can perform Object Extraction from satellite Imagery and yet I don’t where to start. Please, I would like some guidelines to extract specific information from object detected from the satellite imagery. I am stuck in how to start, I am very new in the domain.
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