g_airborne

In terms of accuracy, the authors mention it as a limitation, so probably it could be a problem.

In terms of runtime, it should not matter. Generally speaking though, the overhead of optical flow is often overlooked. For video DL applications, optical flow calculation often takes more time than inference itself. For academic purposes, datasets are often already preprocessed and the optical flow runtime is not mentioned. Doing real time video analysis with optical flow is quite impractical though.

This is very cool, I’ll be studying your implementation of I3D. Did you ever attempt to train I3D end-to-end as done in the Quo Vadis paper? And it so, did you get comparable Top1/Top5 accuracy?

I couldn't agree more, especially with the latter part. I've worked on action recognition with I3D for over a year now, and found that seemingly equivalent implementations in Keras, TensorFlow 2 or PyTorch will produce wildly different results. Worse yet, I found a bunch of papers that will claim SOTA results compared against one of those non-original implementations with just a few percentage-point differences. It makes no sense! It took me hundreds of hours to hunt down the differences between how these frameworks implement their layers before I could come even close to the expected accuracy...

The JIT is hands down the best feature of PyTorch. Especially compared to the somewhat neglected suite of native inference tools for TensorFlow. Just recently I was trying to get a TensorFlow 2 model to work nicely in C++. Basically, the external API for TensorFlow is the C API, but it does not have proper support for `SavedModel` yet. Linking to the C++ library is a pain, and both of them cannot do eager execution at all if you have a model trained in Python code :(

PyTorch will happily let you export your model, even with Python code in it, and run it in C++ :)

It's not that Python is by definition much slower than C++, rather, doing inference in C++ makes it much easier to control exactly when memory is initialised, copied and moved between CPU and GPU. Especially on frame-by-frame models like object detection this can make a big difference. Also, the GIL can be a real problem if you are trying to scale inference on multiple incoming video streams for example.

> The current hardware floor is nearer to the RTX 2080 TI's $1k/unit for 125 tensor-core TFLOPS, and that gives you $25/pflops-d.

It's definitely true that the RTX 2080 Ti would be more efficient money-wise, but the Tensor Cores are not going to get you the advertised speedup. Those speedups can only be reached in ideal circumstances.

Nevertheless, the article as a whole makes a very good point. The thing that is most scary about this is that it would become very hard for new players to enter the space. Large incumbents would be the only ones able to make the investments necessary to build competitive AI. Because of that, I really hope the author isn't right - unfortunately they probably are.

If we’re going down this road of theorizing about the human brain based on DNNs, what is the deal with dropout? Could we help human brains with generalization by randomly removing 10% of our newly created connections at the end of each day to improve long term learning? :)

Is there a way to do this easily on S3 if you’re hosting a static website with custom domain? Last time I checked you still needed to put a CloudFront instance in front of it.

Instead of using Cmd+V to paste, use Cmd+Option+V to cut from the original location and paste. I like it because it lets me postpone the decision to copy or cut until the very end :)

Very cool! Does anyone know how is software support for all these features? It seems that TF doesn’t support the TP32/16 types as of yet. Is this something only CUDA engineers can use right now?

It does seem a little fishy to me that NVIDIA often boasts with figures like 10x performance upgrade whilst in practice those are only possible if you use one of their non-default float types which are hardly supported in most deep learning libraries :(

So this might be an enormous opportunity for low-cost and more performant AI if someone was able to build an FPGA of some sort that could handle these types of computations as efficiently right?

This is very cool! Thanks!

I just replaced my MacBook Late 2013 after similar usage. No repairs, no hiccups or any component failure whatsoever except for a very badly degraded battery - it lasted about 1.5 hours on a full charge at the end. There’s a lot to be said about Apple but honestly I don’t see any other manufacturers that produce laptops that last so insanely long. Hopefully my new one will last as long as well :)