Training a PyTorch Convolutional Neural Network (CNN): Image Folder Dataset vs Numpy.
Challenges
Training a PyTorch convolutional neural network (CNN) using either an image folder dataset or a single numpy array has its own set of pros and cons. The choice between the two largely depends on your specific use case, hardware constraints, and the nature of your data. Here are the pros and cons of each approach:
Training from an Image Folder:
Pros:
Cons:
Training from a Single Numpy Array:
Pros:
Cons:
Summary
The choice depends on hardware constraints, and the nature of your data
In summary, using an image folder dataset is a more common and convenient approach for many computer vision tasks, especially if you have limited memory resources and want to apply on-the-fly data augmentation and preprocessing. However, using a single numpy array can be advantageous for faster data loading and more fine-grained control over data handling if you have the necessary memory capacity.
Ultimately, your choice should be based on your specific project requirements, available hardware, and any constraints you may have. You may also consider hybrid approaches where you preprocess data into Numpy arrays but retain the organizational structure of an image folder dataset for ease of management.
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9 个月Thank you for this. My question is not really related, but in your idea which will give a good result. Training image data using tensorflow or training image data using pytorch?