Datasets:
license: cc-by-nc-sa-4.0
task_categories:
- zero-shot-object-detection
- image-to-text
language:
- en
tags:
- referring-expression-comprehension
- visual-grounding
- text-object-grounding
- ocr
- scene-text
- autonomous-driving
- nuscenes
pretty_name: Read The Object
size_categories:
- 1K<n<10K
dataset_info:
- config_name: nuscenes_referring_expression
features:
- name: id
dtype: string
- name: source
dtype: string
- name: image
dtype: image
- name: image_bbox
dtype: image
- name: category
dtype: string
- name: obj_box
list: float64
length: 4
- name: texts
list: string
- name: combined_text_box
list: float64
length: 4
- name: ref_expression
dtype: string
- name: num_texts_used
dtype: int32
- name: sample_type
dtype: string
- name: attribute
dtype: string
splits:
- name: test
num_bytes: 573221130
num_examples: 1735
download_size: 552935523
dataset_size: 573221130
- config_name: nuscenes_captioning
features:
- name: id
dtype: string
- name: source
dtype: string
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: test
num_bytes: 35505332
num_examples: 259
download_size: 35459986
dataset_size: 35505332
- config_name: nuscenes_text_object_grounding
features:
- name: id
dtype: string
- name: source
dtype: string
- name: image
dtype: image
- name: image_bbox
dtype: image
- name: query_text
dtype: string
- name: categories
list: string
- name: obj_boxes
list:
list: float64
length: 4
splits:
- name: test
num_bytes: 673454364
num_examples: 2107
download_size: 673454364
dataset_size: 673454364
- config_name: nuscenes_text_region_grounding
features:
- name: id
dtype: string
- name: source
dtype: string
- name: image
dtype: image
- name: image_bbox
dtype: image
- name: query_text
dtype: string
- name: boxes
list:
list: float64
length: 4
splits:
- name: test
num_bytes: 668216133
num_examples: 2140
download_size: 668216133
dataset_size: 668216133
- config_name: nuscenes_ocr
features:
- name: id
dtype: string
- name: source
dtype: string
- name: image
dtype: image
- name: image_bbox
dtype: image
- name: texts
list: string
- name: boxes
list:
list: float64
length: 4
splits:
- name: test
num_bytes: 86364012
num_examples: 259
download_size: 86364012
dataset_size: 86364012
configs:
- config_name: nuscenes_referring_expression
data_files:
- split: test
path: nuscenes_referring_expression/test-*
- config_name: nuscenes_captioning
data_files:
- split: test
path: nuscenes_captioning/test-*
- config_name: nuscenes_text_object_grounding
data_files:
- split: test
path: nuscenes_text_object_grounding/test-*
- config_name: nuscenes_text_region_grounding
data_files:
- split: test
path: nuscenes_text_region_grounding/test-*
- config_name: nuscenes_ocr
data_files:
- split: test
path: nuscenes_ocr/test-*
Read The Object
Read The Object is the official data release accompanying the paper "Read the Object: A Dataset and Task for Text-Grounded Object Understanding in Real-World Images" (Chowdhury, Park, Le, Chao, Hayat, Porikli, Mahajan — The Ohio State University, Qualcomm AI Research, Boston University, York University, Vector Institute for AI).
Objects in the real world are often distinguished not by visual appearance alone but by the text printed on them — a traffic sign's route number, a jersey number, a product label. The paper introduces text–object grounding: the task of associating a piece of scene text with the exact object instance it appears on, and shows that current object detectors, OCR systems, and vision-language models all struggle at it despite doing well on text recognition or object localization in isolation. The full benchmark combines images from OpenImages V5, MS COCO, and nuScenes (6,000 images, 25,208 referring-expression samples, 34,085 object instances, 137,246 text instances) and defines six complementary tasks: Referring Expression Comprehension, Text–Object Grounding, OCR, Text-Region Grounding, Visual Question Answering, and Multi-Text Captioning.
This repo currently contains only the nuScenes source category of that
benchmark (the OpenImages V5 and COCO portions, plus the VQA and
Captioning tasks, are not yet part of this HF release — more will be
added incrementally). This is benchmark/evaluation data, not a training
split — every config below ships only a test split.
The dataset is organized by source, then by task, so future source additions (e.g. COCO) slot in as sibling folders under the same layout:
read_the_object/
<source>/
images/ clean source images (shared across tasks)
referring_expression/ REC samples: referring_expression.json + images_bbox/
captioning/ Captioning samples: captioning.json
text_object_grounding/ TOG samples: text_object_grounding.json + images_bbox/
text_region_grounding/ TRG samples: text_region_grounding.json + images_bbox/
ocr/ OCR samples: ocr.json + images_bbox/
Each images_bbox/ image is drawn per-sample (red = target object box,
cyan = the specific text box(es) that sample's row refers to) — not every
box in the source image, only the ones relevant to that row. Captioning
has no images_bbox/, since its output is a whole-image caption rather
than a box.
For datasets/Dataset Viewer users, each task is also published as its own
Parquet config (embedded images, test split):
from datasets import load_dataset
ds = load_dataset("act13/read_the_object", "nuscenes_referring_expression", split="test")
| config_name | task | rows |
|---|---|---|
nuscenes_referring_expression |
Referring Expression Comprehension | 1,735 |
nuscenes_captioning |
Multi-Text Captioning | 259 |
nuscenes_text_object_grounding |
Text–Object Grounding | 2,107 |
nuscenes_text_region_grounding |
Text–Region Grounding | 2,140 |
nuscenes_ocr |
OCR | 259 |
nuscenes/referring_expression/referring_expression.json — Referring Expression Comprehension
Given a natural-language expression that includes both text and visual attributes, locate the described object and its text region. One row per referring-expression sample.
| field | type | description |
|---|---|---|
id |
string | unique sample id, e.g. nuscenes_00000 |
source |
string | "nuscenes" |
image |
string | relative path to the clean image |
image_bbox |
string | relative path to the same image with just this sample's box(es) drawn |
category |
string | object category, supercategory.subtype (e.g. vehicle.bus, signage.traffic_sign) |
obj_box |
[x1,y1,x2,y2] |
target object's bounding box, pixel coords |
texts |
[string, ...] |
the scene-text string(s) used to build the expression |
combined_text_box |
[x1,y1,x2,y2] |
bounding box of the text region(s) used (always contained within obj_box) |
ref_expression |
string | the referring expression, e.g. "the bus with 'stop' written on it" |
num_texts_used |
int | how many text instances were combined into this sample |
sample_type |
string | single_text_single_object, multi_text_single_object, or disambiguated_same_category |
attribute |
string (optional) | present only for disambiguated_same_category samples — a distinguishing visual/spatial attribute added because another object of the same category in the same image shares the same text (e.g. "left blue" in "'23' written on left blue gate") |
nuscenes/captioning/captioning.json — Multi-Text Captioning
Generate a caption that jointly describes the objects in the image and the visible text on them. One row per image, 259 total. Captions are 20-100 words, generated by grounding on the same verified object/text annotations as the other tasks, then manually spot-checked against the source images for accuracy.
| field | type | description |
|---|---|---|
id |
string | unique sample id, e.g. nuscenes_caption_00000 |
source |
string | "nuscenes" |
image |
string | relative path to the clean image |
caption |
string | a 20-100 word caption describing the scene and weaving in the visible scene-text |
nuscenes/text_object_grounding/text_object_grounding.json — Text–Object Grounding
Given a text string, find all objects in the image that contain or are
associated with that text. One row per (image, query_text) pair; if the
same text is shared by multiple objects, all of them are listed (and all
highlighted in image_bbox).
| field | type | description |
|---|---|---|
id |
string | unique sample id, e.g. nuscenes_tog_00000 |
source |
string | "nuscenes" |
image |
string | relative path to the clean image |
image_bbox |
string | relative path to the image with the query text box(es) (cyan) and every linked object box (red) drawn |
query_text |
string | the text string being grounded |
categories |
[string, ...] |
category of each object this text is linked to |
obj_boxes |
[[x1,y1,x2,y2], ...] |
box for each entry in categories, same order |
nuscenes/text_region_grounding/text_region_grounding.json — Text–Region Grounding
Given a query text string, localize the corresponding text region(s) in
the image. One row per (image, query_text) pair; if the same text
appears multiple times in the image, every occurrence is listed (and all
highlighted in image_bbox).
| field | type | description |
|---|---|---|
id |
string | unique sample id, e.g. nuscenes_trg_00000 |
source |
string | "nuscenes" |
image |
string | relative path to the clean image |
image_bbox |
string | relative path to the image with every occurrence of the query text boxed (cyan) |
query_text |
string | the text string being localized |
boxes |
[[x1,y1,x2,y2], ...] |
every box where query_text appears in this image |
nuscenes/ocr/ocr.json — OCR
Detect and recognize all visible text regions in the image. One row per
image; image_bbox shows every verified text box in that image.
| field | type | description |
|---|---|---|
id |
string | unique sample id, e.g. nuscenes_ocr_00000 |
source |
string | "nuscenes" |
image |
string | relative path to the clean image |
image_bbox |
string | relative path to the image with all text boxes drawn |
texts |
[string, ...] |
transcription of each text region in the image |
boxes |
[[x1,y1,x2,y2], ...] |
box for each entry in texts, same order |