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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
image_path: string
label_tree: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 534 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, le (... 483 chars omitted)
child 0, item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 471 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, le (... 420 chars omitted)
child 0, item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 408 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, le (... 357 chars omitted)
child 0, item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 345 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<i
...
int64, (... 93 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, le (... 42 chars omitted)
child 0, item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 30 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: null>>>
child 0, item: struct<cate_index: int64, level: int64, children: list<item: null>>
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: null>
child 0, item: null
to
{'id': Value('string'), 'url': Value('string'), 'image_path': Value('string'), 'union_label_tree': {'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List(Value('null'))})})})})})})})})})}, 'union_label_tree_list': List(List(Value('int64'))), 'union_label_tree_id': List(Value('int64')), 'cate_res': List(Value('string')), 'caption': List(Value('string'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
image_path: string
label_tree: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 534 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, le (... 483 chars omitted)
child 0, item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 471 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, le (... 420 chars omitted)
child 0, item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 408 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, le (... 357 chars omitted)
child 0, item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 345 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<i
...
int64, (... 93 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, le (... 42 chars omitted)
child 0, item: struct<cate_index: int64, level: int64, children: list<item: struct<cate_index: int64, level: int64, (... 30 chars omitted)
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: struct<cate_index: int64, level: int64, children: list<item: null>>>
child 0, item: struct<cate_index: int64, level: int64, children: list<item: null>>
child 0, cate_index: int64
child 1, level: int64
child 2, children: list<item: null>
child 0, item: null
to
{'id': Value('string'), 'url': Value('string'), 'image_path': Value('string'), 'union_label_tree': {'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List({'cate_index': Value('int64'), 'level': Value('int64'), 'children': List(Value('null'))})})})})})})})})})}, 'union_label_tree_list': List(List(Value('int64'))), 'union_label_tree_id': List(Value('int64')), 'cate_res': List(Value('string')), 'caption': List(Value('string'))}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id string | url string | image_path string | union_label_tree dict | union_label_tree_list sequence | union_label_tree_id sequence | cate_res sequence | caption sequence |
|---|---|---|---|---|---|---|---|
bacba1e325b67e1aa48e7ff40f7e80bf/CC3M/images/0110756/0000011.jpg | http://i.dailymail.co.uk/i/pix/2016/09/29/16/38E5EBAB00000578-3814052-image-a-3_1475162870727.jpg | train/001788302.jpg | {
"cate_index": 0,
"level": 0,
"children": [
{
"cate_index": 1,
"level": 1,
"children": [
{
"cate_index": 2,
"level": 2,
"children": [
{
"cate_index": 3,
"level": 3,
"children": [
{
... | [
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[
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[
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[
221,
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3088
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"hair color",
"leaf",
"person",
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"plant",
"view",
"tree",
"sweater",
"woman",
"outdoor",
"hair",
"long hair",
"greenery",
"grass",
"scarf",
"neck",
"eyelash",
"eyebrow",
"shoulder",
"clothing",
"natural object",
"eye",
"scene",
"mouth",
"f... | [
"transformation : these days , looks totally different -- though steering clear of the hair and makeup team from the movie set probably helps",
"a woman wearing a scarf poses for a selfie",
"A woman with blonde hair and a scarf around her neck is smiling at the camera."
] |
bacba1e325b67e1aa48e7ff40f7e80bf/CC3M/images/0107366/0000004.jpg | http://l7.alamy.com/zooms/86e6e4ecd9da4fffb0d3cce148da0be1/view-of-an-illuminated-bathroom-with-a-skylight-a1prbw.jpg | train/000049865.jpg | {
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{
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{
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"children": [
{
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"level": 3,
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{
... | [
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[
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... | [
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1544,
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1812,
1826,
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... | [
"bowl",
"bathtub",
"drawer",
"ceiling",
"tile flooring",
"soap",
"showerhead",
"cabinet",
"towel",
"fruit",
"citrus fruit",
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"modern",
"mirror",
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] | [
"view of an illuminated bathroom with a skylight",
"a modern bathroom with white marble walls and a circular skylight above the bathtub",
"A bathroom with a large circular window above the bathtub."
] |
bacba1e325b67e1aa48e7ff40f7e80bf/CC3M/images/0034051/0000012.jpg | train/001417589.jpg | {
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{
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"children": [
{
... | [
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] | [
"shell",
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"sketch",
"pattern",
"clip art",
"drawing",
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] | [
"a series of icons and interesting figures in pale colors formed in different pattern .",
"paisley pattern in black and white on a green background",
"A pattern of black and white designs on a light green background."
] | |
bacba1e325b67e1aa48e7ff40f7e80bf/CC3M/images/0157859/0000004.jpg | train/001656504.jpg | {
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30... | [
"skin",
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"nose",
"brown",
"indoor",
"hair",
"finger",
"ear",
"arm",
"texture",
"blue",
"chest",
"neck",
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"fabric",
"shoulder",
"green",
"eye",
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"baby clothes",
"face"
] | [
"newborn baby lying on the couch and looking into the camera",
"a baby laying on a couch looking up at the camera - stock photo",
"A baby is lying on a couch with a floral pattern."
] | |
bacba1e325b67e1aa48e7ff40f7e80bf/CC3M/images/0108336/0000015.jpg | http://l7.alamy.com/zooms/0aeaba4a922e44e8ade33cffb22a3265/the-girl-is-sitting-and-doing-sketches-in-her-notebook-on-a-white-ja4t0b.jpg | train/002168431.jpg | {
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{
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"children": [
{
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"children": [
{
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"level": 3,
"children": [
{
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[
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[
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[
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"clothing",
"eye",
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"pencil",
"jacket",
"face"
] | [
"the girl is sitting and doing sketches in her notebook on a white background",
"a young woman sitting on a bench and writing in her notebook",
"A woman is sitting down with a notebook and a pen, looking off to the side."
] |
bacba1e325b67e1aa48e7ff40f7e80bf/CC3M/images/0155241/0000011.jpg | https://media.gettyimages.com/photos/model-walks-the-runway-at-the-holly-fulton-autumn-winter-2015-fashion-picture-id464049818?k=6&m=464049818&s=612x612&w=0&h=JfOFOvl2W9LdwAqbEr-xswRr74G7BH44scONyAY7MEo= | train/002259465.jpg | {
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"children": [
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"children": [
{
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[
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],
[
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[
266,
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221,
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... | [
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"footwear",
"nose",
"jewelry",
"fashion model",
"hip",
"knee",
"waist",
"floor",
"indoor",
"bone",
"hair",
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"finger",
"long hair",
"catwalk",
"ear",
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"wrist",
"neck",
"hand",
"head",
"eyelash",
"eyebrow",
"shoulder",
"clothing",
... | [
"a model walks the runway at the winter fashion show during event .",
"a model walks down the runway wearing a green dress",
"A woman wearing a green and pink sequin dress walks down a runway."
] |
bacba1e325b67e1aa48e7ff40f7e80bf/CC3M/images/0044444/0000005.jpg | train/001993851.jpg | {
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{
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"children": [
{
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"children": [
{
... | [
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[
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],
[
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],
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],
[
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],
[
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],
[
867,
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],
[
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],
[
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] | [
"island",
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"sea",
"view",
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End of preview.
HiTag Dataset
HiTag is a benchmark for hierarchical image tagging, where an image is annotated not only with fine-grained semantic tags but also with their corresponding hierarchical relationships. The benchmark contains a large-scale training set constructed from CC3M and a test set constructed from OpenImages. HiTag provides hierarchical annotations for evaluating image tagging models at multiple semantic levels.
Dataset Structure
The dataset is provided in JSONL format.
Training Set
The training data are constructed from CC3M using our annotation pipeline and contain hierarchical annotations.
The training annotations contain the following representative fields:
id: sample identifierurl: original image URLimage_path: image pathcaption: image captionunion_label_tree: hierarchical tag annotationsunion_label_tree_id: hierarchical tag IDs
Test Set
The test set is constructed from OpenImages and annotated for hierarchical image tagging evaluation.
The test annotations use a different schema from the training data because the training and test sets are constructed from different source datasets and serve different purposes.
Representative fields include:
image_path: image pathlabel_tree: annotated hierarchical tag structure
Hierarchical Tag Structure
HiTag organizes image tags into a tree-structured semantic hierarchy that includes category names and definitions. The hierarchy will be released soon.
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