Creating Tensorflow Dataset for Object Recognition in Keras

Hi,

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I was wondering if someone could aid me in solving this problem. I have been following this tutorial, which uses the COCO dataset from tfds.

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I am interested in using a different dataset, but I am having trouble adapting the code.

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My dataset consists of a number of images, with corresponding bounding box annotations in a .csv. It can be summarized as this: [filename, xmin, ymin, xmax, ymax, class].

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The code in this tutorial uses (to my understanding) a tensor dataset with the format [image_array, xmin, ymin, xmax, ymax, class].

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How can I load this data in this format? I have been having a great deal of trouble finding any resources. Any help is greatly appreciated! I will mention how I have been approaching this below.

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Summary of things applied:

Essentially, I have been able to load everything into a pandas dataframe with 6 columns, consisting of [filename, xmin, ymin, xmax, ymax, class]. However, I feel this to be inefficient, and I cannot get the last step (conversion to a tensor).

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I try: d = tf.data.Dataset.from_tensors((df.values))

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and get: ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).