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Comment by p1esk | Hacker News Reader

I don't get it - is there anything preventing you using non-square images today?

Just the massive inconvenience of having to track down and implement the changes necessary to add non-square image support on most platforms. So if you think that is a minor thing that can be dismissed as nothing, then no. Or one can always crop, or resize, both of which throw away information, or pad, for which the underlying behavior is undocumented. I just don’t think any of these are great options. Do you?

If the answer is “you can always write your own” that is true, but it’s just underlining my point that the problem is not yet solved.

support on most platforms

What do you mean by "platforms"?

Deep learning frameworks: Keras, Pytorch, Tensorflow, Core ML, etc.

You might be confused. There's nothing in any of those platforms that dictate you use square images.

It's possible I am confused, yes. But I'm just going by what is in all the documentation and tutorials I have encountered.

It may have been solved in a lab somewhere, but the solution hasn't made it out into code usable by mere mortals, as far as I can tell. You may be applying a very special meaning of the words "nothing" and "dictate" it's just that it's very well hidden how to do this.

I'm not alone. Here are examples of other people struggling with non-square images and not succeeding:

https://stats.stackexchange.com/questions/240690/non-square-...

https://github.com/tanakataiki/ssd_kerasV2/issues/10

https://github.com/allanzelener/YAD2K/issues/51

https://github.com/eriklindernoren/PyTorch-YOLOv3/issues/277

https://stackoverflow.com/questions/49893741/tensorflow-cnn-...

https://github.com/ml-hongkong/keras-transfer-learning-for-o...

You are confusing platforms with individual models. A platform, such as Pytorch or Tensorflow, does not care what shape is your input. You design and train a model for the whatever inputs you want. On the other hand, if someone trained a specific model architecture (e.g. YOLOv3 or Resnet-50) on some dataset of square images, then yes, this particular pre-trained model will expect the same input size and shape as what it was trained on. Does it make sense? If you take a beginner level course on deep learning (e.g. Stanford CS231n or FastAI course) you will immediately realize there's nothing (in any sense of the word) that prevents you from using any input shape to train your model.

If you don't want to learn the tools you use, you will need to find someone who will train a model on your images. If you're willing to pay for it you will find plenty of help. However, if you want to neither learn nor pay, then what exactly are you complaining about?

No, in production rectangle images are reshaped and resized to square ones.

If you want to be able to run inference in batches, you have to normalize to a given input size. It often means a square input size if you want to minimally distort both vertical and horizontal images.

You can also use variable input shape in production if you need to. It will be harder to optimize, especially for throughput, but it's not impossible.

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