Aleksandar/nearid-siglip2
Image Feature Extraction • Updated • 3
Datasets:
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
id: int64
category: string
category_description: string
images1: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
images2: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
images3: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
masks1: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
masks2: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
masks3: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
n_images: int64
objaverse_id: string
dino_01: double
dino_02: double
dino_12: double
aesthetics1: double
aesthetics2: double
aesthetics3: double
size1: list<element: int64>
child 0, element: int64
size2: list<element: int64>
child 0, element: int64
size3: list<element: int64>
child 0, element: int64
prompts1: string
prompts2: string
prompts3: string
filenames1: string
filenames2: string
filenames3: string
quality: string
id_safe: bool
split: string
-- schema metadata --
huggingface: '{"info": {"features": {"id": {"dtype": "int64", "_type": "V' + 1418
to
{'id': Value('int64'), 'category': Value('string'), 'category_description': Value('string'), 'img1': Image(mode=None, decode=True), 'img2': Image(mode=None, decode=True), 'img3': Image(mode=None, decode=True), 'n_images': Value('int64'), 'objaverse_id': Value('string'), 'prompts1': Value('string'), 'prompts2': Value('string'), 'prompts3': Value('string'), 'quality': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 209, in _generate_tables
yield Key(file_idx, batch_idx), self._cast_table(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 147, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: int64
category: string
category_description: string
images1: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
images2: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
images3: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
masks1: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
masks2: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
masks3: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
n_images: int64
objaverse_id: string
dino_01: double
dino_02: double
dino_12: double
aesthetics1: double
aesthetics2: double
aesthetics3: double
size1: list<element: int64>
child 0, element: int64
size2: list<element: int64>
child 0, element: int64
size3: list<element: int64>
child 0, element: int64
prompts1: string
prompts2: string
prompts3: string
filenames1: string
filenames2: string
filenames3: string
quality: string
id_safe: bool
split: string
-- schema metadata --
huggingface: '{"info": {"features": {"id": {"dtype": "int64", "_type": "V' + 1418
to
{'id': Value('int64'), 'category': Value('string'), 'category_description': Value('string'), 'img1': Image(mode=None, decode=True), 'img2': Image(mode=None, decode=True), 'img3': Image(mode=None, decode=True), 'n_images': Value('int64'), 'objaverse_id': Value('string'), 'prompts1': Value('string'), 'prompts2': Value('string'), 'prompts3': Value('string'), 'quality': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
NearID — Multi-View Identity Dataset
This is the base positives dataset for the NearID project. Each sample contains multiple views of the same identity rendered in different backgrounds/contexts.
Near-identity distractors (different but similar instances in matched context) are available as separate datasets listed below. Together, they form the NearID training and evaluation benchmark.
Quick Start
from datasets import load_dataset
# Load base positives
ds = load_dataset("Aleksandar/NearID")
# Load a negative source for contrastive training/evaluation
neg = load_dataset("Aleksandar/NearID-Flux")
Dataset Structure
| Column | Type | Description |
|---|---|---|
id |
int64 | Sample ID (shared across all NearID datasets) |
category |
string | Object category |
category_description |
string | Natural language description of the identity |
img1, img2, img3 |
image | Multi-view images of the same identity in different contexts |
n_images |
int64 | Number of valid views |
objaverse_id |
string | Source Objaverse object identifier |
prompts1–prompts3 |
string | Generation prompts for each view |
quality |
string | Quality label |
All NearID Datasets
| Dataset | Description | Resolution |
|---|---|---|
| Aleksandar/NearID | Multi-view positives (anchor + positive views) | Base |
| Aleksandar/NearID-Flux | Near-identity distractors via FLUX.1 inpainting | 512×512 |
| Aleksandar/NearID-Flux_1024 | Near-identity distractors via FLUX.1 inpainting | 1024×1024 |
| Aleksandar/NearID-FluxC | Near-identity distractors via FLUX.1 Canny-guided inpainting | 512×512 |
| Aleksandar/NearID-FluxC_1024 | Near-identity distractors via FLUX.1 Canny-guided inpainting | 1024×1024 |
| Aleksandar/NearID-PowerPaint | Near-identity distractors via PowerPaint inpainting | 512×512 |
| Aleksandar/NearID-Qwen | Near-identity distractors via Qwen-based inpainting | 512×512 |
| Aleksandar/NearID-Qwen_1328 | Near-identity distractors via Qwen-based inpainting | 1328×1328 |
| Aleksandar/NearID-SDXL | Near-identity distractors via Stable Diffusion XL inpainting | 512×512 |
| Aleksandar/NearID-SDXL_1024 | Near-identity distractors via Stable Diffusion XL inpainting | 1024×1024 |
Related
- Model: Aleksandar/nearid-siglip2 — NearID identity embedding model
- Paper: NearID: Identity Representation Learning via Near-identity Distractors
- Code: github.com/Gorluxor/NearID
License & Attribution
This dataset is released under CC-BY-4.0. It is derived from the SynCD dataset (MIT License, Copyright 2022 SynCD). If you use this dataset, please cite both NearID and SynCD.
Citation
@article{cvejic2026nearid,
title={NearID: Identity Representation Learning via Near-identity Distractors},
author={Cvejic, Aleksandar and Abdal, Rameen and Eldesokey, Abdelrahman and Ghanem, Bernard and Wonka, Peter}
}
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Size of downloaded dataset files:
2.82 GB
Size of the auto-converted Parquet files:
2.82 GB
Number of rows:
19,386
Models trained or fine-tuned on Aleksandar/NearID