YOLOv5 to Tensorflow Lite

[Christalker123]

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Hi! I trained YOLOv5 model. I validated it and tested it using detect.py. and the prediction really works perfectly. So I converted the model into tensorflow lite using this 'python export.py --weights yolov5s.pt --include tflite'. Then i integrated the tflite into android studio and so when i tested it. It didn't work correctly not same as the testing before it was converted. Is this really a problem? Theoretically maybe i should convert it to onnx first and then tensorflow lite. So the question is it gonna affect the accuracy of the model if we converted it to tflite?

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[glenn-jocher]

@Christalker123 hi! Conversion to TensorFlow Lite from PyTorch is still experimental, so it is expected that the model may not perform the same after conversion. Additionally, it's recommended to use the official TensorFlow Lite converter to convert models to TFLite format.
Regarding converting to ONNX before TensorFlow Lite, it may not necessarily affect the accuracy of the model. Each conversion may result in some loss of precision, but it's hard to predict how much it will affect the accuracy of the model in practice. I recommend testing and validating the performance of the TFLite model after conversion to ensure that it meets your requirements.

[Christalker123]

@glenn-jocher Hi thank you for the response sir. May i ask a question sir?

During training sir of my mode and the .yaml file formation of the classes is like this:

nc: 10
names: ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']

then when i convert my model.pt into model.tflite

so the label txt file should be also like this: ?

names: ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']

or its ok if its like this :
one
two
three
four
five
six
seven
eight
nine
or this formation may cause class indices and labels will not match. And i think this is the reason why it didn't work properly
Thank you in advance for the response!

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[glenn-jocher]

@Christalker123 Hello! Your label txt file should indeed match the exact class names and order as specified in the .yaml file, such as names: ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'].

Using anything other than this format in the label txt file could cause class indices and labels to not match, potentially resulting in issues during inference. Check that your label txt file is structured correctly, and ensure that the class indices and labels align in both the .yaml file and the label txt file. Good luck!