Python Pytorch Tutorials # 1 Transfer Learning : DataLoaders Pytorch

Python Pytorch is another somewhat newer, deep learning framework, which I am finding to be more intuitive than the other popular framework Tensorflow.
In this mini series in the first set of Python Pytorch tutorials, of transfer learning with Pytorch we will first learn about learn about data preparation using DataLoaders.
In additional we will learn about data transformation using Transformers.

Before data can be feed into a model, it needs to be "prepared" or preprocessed. This preprocessing makes it easier for the model to learn, thus the preprocessing.

Code will be added to my Github once I set up git on my other PC:

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[PyMoondra]

Let me know if quality is ok. Thank you.

[physicsandtechnology]

the best explanation ever on PyTorch not much complex

[barulli]

Yo! great video! can you show us more on writing custom dataloaders? specifically on different cases of folder set up, images, labels, masks etc..
cheers!

[GeNiaaz]

Hi, I really enjoyed the video, but I do have a question. My data is not like yours whereby the images are separated by type. To give an example similar to yours, my data is like if there's an image with multiple cats and dogs in one picture, but I have already marked the coordinates of the bounding boxes surrounding the individual cats and dogs in a CSV file, and labelled them as such in the CSV. Do you have any idea how I could instruct the pytorch model to read in the data ?

[SuiGio]

Could you possibly do a video about data augmentation? Would be awesome! Thank you for this amazing content, keep it up!

[aarababderrazzak]

hallo thx for your work, can you use that with Kitti dataset ?