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I'm interested in training a YOLOv5 model. Currently, I'm using Roboflow to annotate and export the data into YOLOv5 format. I'm also using Roboflow's Colab Notebook for YOLOv5.

However, I'm not familiar with many of the commands used in the Roboflow Colab Notebook. I found on here that there appears to be a much more "Pythonic" way of using and manipulating the YOLOv5 model, which I would be much more familiar with.

My questions regarding this are as follows:

  1. Is there an online resource that can show me how to train the YOLOv5 and extract results after importing the model from PyTorch with the "Pythonic" version (perhaps a snippet of code right here on StackOverflow would help)? The official documentation that I could find (here) also uses the "non-Pythonic" method for the model.
  2. Is there any important functionality I would lose if I were to switch to this "Pythonic" method of using YOLOv5?
  3. I found nothing in the documentation that suggests otherwise, but would I need to export my data in a different format from Roboflow for the data to be able to train the "Pythonic" model?
  4. Similar to question 1), is there anywhere that can guide me how to use the trained model on test images? Do I simply do prediction=model(my_image.jpg)? What if I want predictions on multiple images at once?

Any guidance would be appreciated. Thanks!

user16264465
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1 Answers1

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You can use the GitHub repository of ultralytics to do what you want; if you want to understand the process, check out the train.py file to get a better understanding. There isn't a straightforward explanation you just have to learn by yourself.

For the training: if you want to write the code by yourself it will need a lot of ML knowledge; that's why train.py exist, same for test.py and export.py.

d1sh4
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tchikyy
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