sqlglot VS pytorch-image-models

This is probably referring to "zero changes to your driver code" and not "zero changes to the SQL you send over this driver".

Translating between SQL dialects is notoriously hard and attempts to translate [1] are working in 95% of cases. But the last 5% would require 5x amount of work. That's because "SQL dialect" also includes weird edge cases of type inference of things like COALESCE(5, FALSE) and emulation of system catalogs (pg_catalog, information_schema).

[1] https://github.com/tobymao/sqlglot

Recommend checking out https://github.com/tobymao/sqlglot if you are interested in this capability for other SQL dialects

Tools like this are helpful for:

- Rendering SQL in a consistent way, eg for snapshot testing

sqlglot – Python SQL Parser and Transpiler

In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery.

Cool! Is this built with sqlglot[1] on the back end?

[1] https://github.com/tobymao/sqlglot

Wanted to give sqlglot a shoutout as it saved me a ton of time.

I am a PyTorch user I work in CV, I usually use the PyTorch models. However, I see people use timm in research papers to train their models I don't understand what it is timm is it a new framework like PyTorch? Further, when I click https://pypi.org/project/timm/ homepage it takes me to hugging face GitHub https://github.com/huggingface/pytorch-image-models is there any connection between timm and hugging face many of my friends use hugging face but I also don't know about hugging face I use simple PyTorch and torchvision.models.

https://github.com/huggingface/pytorch-image-models https://huggingface.co/docs/timm/index

pytorch-image-models

additionally, you might find the timm library handy for this sort of work.

This is still being pursued. Ross Wightmann's timm[0,1] package (now on Hugging Face) has done a lot of this. There's also a V2 of ConvNext[2]. Ross does write about this a lot on Twitter fwiw. I should also mention that there are still many transformer based networks that still beat convs. So there probably won't be a resurgence in convs until someone can show that there's a really strong reason for them. They have some advantages but they also might not be flexible enough for the long range tasks in segmentation and detection. But maybe they are.

FAIR definitely did great work with ConvNext, and I do hope to see more. There always needs to be people pushing unpopular paradigms.

[0] https://github.com/huggingface/pytorch-image-models

[1] https://arxiv.org/abs/2110.00476

[2] https://arxiv.org/abs/2301.00808

I am doing Binary classification with a pre-trained EfficientNet tf_efficientnet_l2. I froze all weights during training and replaced the classifier with a custom trainable one that looks like:

In this post, I’m going to show you how you can pick from over 900+ SOTA models on TIMM, train them using best practices with Fastai, and deploy them on Android using Flutter.

The other thing is, try to find tricks to speed up your experiments (if not having done so already). The most obvious are mixed precision training, have your model train on a lower resolution input first and then increase the resolution later in the training, stochastic depth, and a bunch more stuffs. Look for implementations in https://github.com/rwightman/pytorch-image-models .