raise core._status_to_exception(e) from None tensorflow.python.framework.errors_impl.InvalidArgumentError:

I faced this same issue when I tried to train efficientDet-object-detection model, from exported datasets as tfrecord from Roboflow. Here's how I solve this:

This error means your generated tf-record files are corrupted. Use this following script to check the state of the tf-records:

import tensorflow as tf

def is_tfrecord_corrupted(tfrecord_file):
    try:
        for record in tf.data.TFRecordDataset(tfrecord_file):
            # Attempt to parse the record
            _ = tf.train.Example.FromString(record.numpy())
    except tf.errors.DataLossError as e:
        print(f"DataLossError encountered: {e}")
        return True
    except Exception as e:
        print(f"An error occurred: {e}")
        return True
    return False

# Replace with your TFRecord file paths 
tfrecord_files = ['your_test_record_fname', 'your_train_record_fname']

for tfrecord_file in tfrecord_files:
  if is_tfrecord_corrupted(tfrecord_file):
      print(f"The TFRecord file {tfrecord_file} is corrupted.")
  else:
      print(f"The TFRecord file {tfrecord_file} is fine.")

To fix the corrupted tfrecords, I exported the datasets as pascal-voc format, and then I wrote the following script hosted here on GitHub to generate new tfrecords from the pascal-voc formatted dataset.

Script to generate new tfrecords is here: https://github.com/arrafi-musabbir/license-plate-detection-recognition/blob/main/generate_tfrecord.py

• Create your own label-map-pbtxt according to your dataset:

label_path = "your label_map.pbtxt path"

# modify according to your dataset class names
labels = [{'name':'license', 'id':1}]

with open(label_path, 'w') as f:
    for label in labels:
        f.write('item { \n')
        f.write('\tname:\'{}\'\n'.format(label['name']))
        f.write('\tid:{}\n'.format(label['id']))
        f.write('}\n')

• Run the script as the following:

python generate_tfrecord.py -x {train_dir_path} -l {labelmap_path} -o {new_train_record_path}
python generate_tfrecord.py -x {valid_dir_path} -l {labelmap_path} -o {new_valid_record_path}
python generate_tfrecord.py -x {test_dir_path} -l {labelmap_path} -o {new_test_record_path}

Afterwards, run the is_tfrecord_corrupted(tfrecord_file) again and you will see that the tfrecords are fine.