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

by natch 6y ago

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?

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

by 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?