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 mo

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?