tensorflow/models · error · ValueError
{} is not unique across image directories
Error message
{} is not unique across image directories What it means
Error "{} is not unique across image directories" thrown in tensorflow/models.
Source
Thrown at official/vision/data/create_coco_tf_record.py:311
num_annotations_skipped: Number of (invalid) annotations that were ignored.
Raises:
ValueError: if the image pointed to by data['filename'] is not a valid JPEG,
does not exist, or is not unique across image directories.
"""
image_height = image['height']
image_width = image['width']
filename = image['file_name']
image_id = image['id']
if len(image_dirs) > 1:
full_paths = [os.path.join(image_dir, filename) for image_dir in image_dirs]
full_existing_paths = [p for p in full_paths if tf.io.gfile.exists(p)]
if not full_existing_paths:
raise ValueError(
'{} does not exist across image directories.'.format(filename))
if len(full_existing_paths) > 1:
raise ValueError(
'{} is not unique across image directories'.format(filename))
full_path, = full_existing_paths
# If there is only one image directory, it's not worth checking for existence,
# since trying to open the file will raise an informative error message if it
# does not exist.
else:
image_dir, = image_dirs
full_path = os.path.join(image_dir, filename)
with tf.io.gfile.GFile(full_path, 'rb') as fid:
encoded_jpg = fid.read()
feature_dict = tfrecord_lib.image_info_to_feature_dict(
image_height, image_width, filename, image_id, encoded_jpg, 'jpg')
num_annotations_skipped = 0
if bbox_annotations:
box_feature_dict, num_skipped = bbox_annotations_to_feature_dict(View on GitHub (pinned to e006f5f0d5)
When it happens
Trigger: Thrown at official/vision/data/create_coco_tf_record.py:311 when the library encounters an invalid state.
Common situations: See trigger scenarios.
AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24).
Data as JSON: /api/errors/48a4f45c20c7b715.
Report an issue: GitHub.