I am following along this tutorial (https://colab.research.google.com/github/khanhlvg/tflite_raspberry_pi/blob/main/object_detection/Train_custom_model_tutorial.ipynb) from Colab and running it on my own Windows machine.

When I debug my script it throws me this error > The size of the train_data (0) couldn't be smaller than batch_size (4). To solve this problem, set the batch_size smaller or increase the size of the train_data.

On this snippet of my code

model = object_detector.create(train_data, model_spec=spec, batch_size=4, train_whole_model=True, epochs=20, validation_data=val_data)

My own train data contains 101 images and the example from Colab only contains 62 in their training folder.

I understand it's complaining about training data can't be smaller than batch size but I don't understand why its throwing it in the first place since my training data is not empty.

On my own machine I have Tensorflow Version: 2.8.0 just like in the colab.

I've tried increasing batch sizes all the way from 0 to 100plus but stil gives me the same error.

I've tried dropping one sample so there are 100 images and setting sample size to 2 , 4 etc... but still throws the error.

I'm leading to the conclusion that it is not loading in the data correctly but why?

CodePudding user response：

For anybody running into the same issue as I was , here was my solution.

Okay so the reason this is happening is because of different versions of Python.

I was trying to run this locally with Python 3.8.10

Colab is running 3.7.12 .

I ran all of my data on colab using version (3.7.12) and trained my model with no more further issues.