I created an implementation of Andrej Karpathy’s micrograd demo running inside the browser in pyscript. This trains a mini neural net inside the browser using his micrograd library.
This page allows you to run it interactively like you can with a Jupyter notebook, except this is running inside of your browser. https://matdmiller.github.io/fastai-huggingface-sample-web-app1/pyscript-micrograd-demo.html
This page automatically runs the entire script and displays the training graphic at the end when it’s done. I wanted to test running this demo on my iPhone, but couldn’t run the Jupyter/REPL cells on my iPhone because I can’t click Shift+Enter to run them so this just automatically runs the entire script. Amazingly, it works! https://matdmiller.github.io/fastai-huggingface-sample-web-app1/pyscript-micrograd-demo-autorun.html
More epochs yields better (100% accuracy) accuracy, but the speed/performance of this is not great, so I cut it down to 20 epochs so it finishes faster.
Wow! amazing work! it took a couple minutes to run on my 12 year old macbook air running linux (in Chromium) … amazing to see this running in a browser.
For this first project, I wanted to try something a bit more abstract than just objects. Here, I train a model to classify the four seasons from images obtained from Duck Duck Go:
Spring
Summer
Fall
Winter
In terms of hyperparamaters, here are some adjustments that seemed helpful:
Architecture: I tried resnet18, resnet34, resnet50 and resnet101. With smaller training sets of 100-200 images/season, the smaller architectures outpreformed the larger ones—as high as 90-95% accuracy. I pursume this might in part be due to overtraining. Additionally, the smaller sets of images are likely have been more homogeneous than the larger training sets. For the final model I used resnet101 and 750 images/season with resulting accurcay of 80-85%.
Number of Pictures: 150 training images/season were initially used. These trained very well. As I increased the training sizes, performance dropped. I believe that this is because the larger training sets were much more diverse. I could verify this by looking manually at the DDG search results: later images diverged significantly from earlier images. For the final model, I used 750 training images/season. These larger training sets did not perform as well. Hopefully, the larger and more diverse training sets will perform better In the Wild, with real test data.
Image Size: 400 size images performed better than 300 size images
Transformations: squish outperformed crop
Training Epochs: 5 training epochs with resnet101 showed continued reduction in both training and validation losses
Cool HF demo. I noticed that the inference can take a really long time (upwards of 20 seconds). Do you have a sense of why that is? Is it on account of the model being large (i.e. fine-tuned off resnet101) or are the sample images large or is there something else going on? Curious if there are some tips on how to keep that inference time low.
I’ve noticed the lag as well. Moreover, it’s quite variable. Picture size, network/server load at HuggingFace, not sure. It is free. I would guess the paid models perform better. Frankly, I had never heard of either Gradio or HuggingFace till yesterday reading through the forums. Very cool
I wrote up my attempt at training a model to recognise my very cute (but not particularly intelligent) cat, Mr Blupus.
I ran into exactly the problem with validation data that Jeremy describes in chapter one, where my model was ‘cheating’ by learning to recognise my house (i.e. the photo backgrounds) and not my cat.
I reran the experiment with a held-out test set and surprisingly the model still did pretty well. I’m not sure if I did something wrong with how I calculated the accuracy using the test set, but in any case it was a useful lesson to have experienced in a practical way. (Thanks for the help with the final step, @FraPochetti and to those in the delft-fastai study group who encouraged me to collect a held-out test set.)
All we knew that he was soft when we ‘rescued’ him off the street. He was initially called ‘minicat’ because he was so tiny, but his true character soon revealed itself.
Looking to build, and then share, a recommendation system on this forum using the collaborative filtering packages available in fastai. However, I’m seeking your help to collect the necessary data.
The purpose of the recommendation system is to recommend travel destinations based on individual user profiles. The drive to develop such a model stems from the fact that my fiance and I are getting married at the end of the year, and we’re unsure where we’d like to go for our honey moon. The recommendation system will therefore provide a list of country recommendations based on our individual user profiles.
My kid and I keep on surfing the internet for various dinosaurs and their features and habitats and nesting patterns.
He also builds dinos from his lego blocks and building blocks, occasionally does drawing of them.
Recently he is working on building a Jurassic world with all his toys.
I thought it will be fun to build a prehistoric dinosaur identifier for this week’s homework for him to play around with.
I used seresnext50 model and fine-tuned it for 20 epochs.
I was able to achieve 0.248869 error rate. Blog post for more detailed analysis is under construction.
Had to look up what that is. That’s an interesting idea. No idea how I’d implement that with fastai, however. From what I remember of the course and the library, there are some helper functions built into fastai that will at least allow me to determine which examples it found hardest to determine. That might be a start in this general direction of looking under the hood.
