Pytorch tutorials

Find Notes for the papers here

• Many codes are using Pytorch Lightning. More will follow. Eventually I might shift any important ones to it. Why? Well. It did what I wanted to with this repository in terms of standardization. Why be redundant.
• Make it easy for anyone to understand huge libraries by taking small examples and breaking it up in that way
• End up with boilerplate code for a variety of tasks
• Note that this is work in progress. And many more folders will be added in the coming days

How to use this repo?

• Look for whats new. Since most of the code remains the same, there are only a few bits that change. Find them. And youll understand.
• Search for the "#This is new" comment
• How do you find whats new? Look at the basicNet repo. This is the starter template. Everything comes from there.
• What about GANs? Look at DCGAN. It is the basic one
• Papers? Well you can refer to my notes repo. (above) or you can just read the paper (I recommend this )

There are a billion similar repositories, what makes this different?

• Want to understand research papers? Check out my other repo Link
• Comments!!
• Standardized (mostly) code. Aka I try to be as uniform in my approach as possible
• Might write a few blog posts
• To be very honest, it is for my practise xD

FAQ

• What is readmegen.sh?? Well its a bash script which takes a template and adds a folder wise listing so its easy to see whats been done. (I am lazy come on.)
• Can I use my own dataset? Yes of course. Just modify the dataloader. (I will be adding tutorials for that soon either way)
• No distributed training. I have only one GPU (sad) so I wont be using it
• What is the accuracy? Well run it and find out. I cant train everything to its maximum capacity due to lack of time
• Why so many files? I wonder the same sometimes. But its easier to modify for other data. And keeps my errors easily identifiable
• Why do you have so many arguments? Most of them arent even used? This is because I wanted to "standardize the codes". So I dont remove much. Just add what I need. That way its easier to see the difference between things. Just remove it if you dont want it there. (If it doesnt break anything)

References

All the sites I referred to for the codes here (heavily modified so you probably wont find the same things but here for future reference just in case anyone wants it). I have to admit that I relied pretty heavily on these repos. Do have a look at them if you are interested.

• pytorch/examples
• pytorch/tutorials
• usuyama
• GANS

What can you find in this repo?

• ./Unet/
• ./archive/
• ./archive/Augmentation/
• ./archive/GAN/
• ./archive/Misc/
• ./archive/TrainingParams/
• ./archive/applications/
• ./archive/applications_/
• ./archive/architectures/
• ./archive/architectures_/
• ./convnext/
• ./deepDream/
• ./deepDream/outputs/
• ./deepDream/test_ims/
• ./gradcam/
• ./random_experiments/
• ./visionTransformer/
• ./visionTransformer/chpts/