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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 th

by natch 6y ago

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.

p1esk 6y ago
support on most platforms What do you mean by "platforms"?
- natch 6y ago
  Deep learning frameworks: Keras, Pytorch, Tensorflow, Core ML, etc.
  - p1esk 6y ago
    You might be confused. There's nothing in any of those platforms that dictate you use square images.
    - natch 6y ago
      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-images-for-image-classification https://stats.stackexchange.com/questions/240690/non-square-... https://github.com/tanakataiki/ssd_kerasV2/issues/10 https://github.com/tanakataiki/ssd_kerasV2/issues/10 https://github.com/allanzelener/YAD2K/issues/51 https://github.com/allanzelener/YAD2K/issues/51 https://github.com/eriklindernoren/PyTorch-YOLOv3/issues/277 https://github.com/eriklindernoren/PyTorch-YOLOv3/issues/277 https://stackoverflow.com/questions/49893741/tensorflow-cnn-for-non-square-image https://stackoverflow.com/questions/49893741/tensorflow-cnn-... https://github.com/ml-hongkong/keras-transfer-learning-for-oxford102/issues/3 https://github.com/ml-hongkong/keras-transfer-learning-for-o...
      - p1esk 6y ago
        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?

by natch 6y ago

p1esk 6y ago

support on most platforms What do you mean by "platforms"?

natch 6y ago
Deep learning frameworks: Keras, Pytorch, Tensorflow, Core ML, etc.
- p1esk 6y ago
  You might be confused. There's nothing in any of those platforms that dictate you use square images.
  - natch 6y ago
    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-images-for-image-classification https://stats.stackexchange.com/questions/240690/non-square-... https://github.com/tanakataiki/ssd_kerasV2/issues/10 https://github.com/tanakataiki/ssd_kerasV2/issues/10 https://github.com/allanzelener/YAD2K/issues/51 https://github.com/allanzelener/YAD2K/issues/51 https://github.com/eriklindernoren/PyTorch-YOLOv3/issues/277 https://github.com/eriklindernoren/PyTorch-YOLOv3/issues/277 https://stackoverflow.com/questions/49893741/tensorflow-cnn-for-non-square-image https://stackoverflow.com/questions/49893741/tensorflow-cnn-... https://github.com/ml-hongkong/keras-transfer-learning-for-oxford102/issues/3 https://github.com/ml-hongkong/keras-transfer-learning-for-o...
    - p1esk 6y ago
      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?