Datasets:
metadata
license: cc-by-nc-4.0
task_categories:
- image-classification
- object-detection
language:
- zh
- en
tags:
- tt100k
- traffic signs
pretty_name: TT100K
size_categories:
- 10K<n<100K
dataset_info:
- config_name: default
features:
- name: image
dtype: image
- name: objects
sequence:
- name: category
dtype:
class_label:
names:
'0': i1
'1': i10
'2': i11
'3': i12
'4': i13
'5': i14
'6': i15
'7': i2
'8': i3
'9': i4
'10': i5
'11': il100
'12': il110
'13': il50
'14': il60
'15': il70
'16': il80
'17': il90
'18': io
'19': ip
'20': p1
'21': p10
'22': p11
'23': p12
'24': p13
'25': p14
'26': p15
'27': p16
'28': p17
'29': p18
'30': p19
'31': p2
'32': p20
'33': p21
'34': p22
'35': p23
'36': p24
'37': p25
'38': p26
'39': p27
'40': p28
'41': p3
'42': p4
'43': p5
'44': p6
'45': p7
'46': p8
'47': p9
'48': pa10
'49': pa12
'50': pa13
'51': pa14
'52': pa8
'53': pb
'54': pc
'55': pg
'56': ph1.5
'57': ph2
'58': ph2.1
'59': ph2.2
'60': ph2.4
'61': ph2.5
'62': ph2.8
'63': ph2.9
'64': ph3
'65': ph3.2
'66': ph3.5
'67': ph3.8
'68': ph4
'69': ph4.2
'70': ph4.3
'71': ph4.5
'72': ph4.8
'73': ph5
'74': ph5.3
'75': ph5.5
'76': pl10
'77': pl100
'78': pl110
'79': pl120
'80': pl15
'81': pl20
'82': pl25
'83': pl30
'84': pl35
'85': pl40
'86': pl5
'87': pl50
'88': pl60
'89': pl65
'90': pl70
'91': pl80
'92': pl90
'93': pm10
'94': pm13
'95': pm15
'96': pm1.5
'97': pm2
'98': pm20
'99': pm25
'100': pm30
'101': pm35
'102': pm40
'103': pm46
'104': pm5
'105': pm50
'106': pm55
'107': pm8
'108': pn
'109': pne
'110': po
'111': pr10
'112': pr100
'113': pr20
'114': pr30
'115': pr40
'116': pr45
'117': pr50
'118': pr60
'119': pr70
'120': pr80
'121': ps
'122': pw2
'123': pw2.5
'124': pw3
'125': pw3.2
'126': pw3.5
'127': pw4
'128': pw4.2
'129': pw4.5
'130': w1
'131': w10
'132': w12
'133': w13
'134': w16
'135': w18
'136': w20
'137': w21
'138': w22
'139': w24
'140': w28
'141': w3
'142': w30
'143': w31
'144': w32
'145': w34
'146': w35
'147': w37
'148': w38
'149': w41
'150': w42
'151': w43
'152': w44
'153': w45
'154': w46
'155': w47
'156': w48
'157': w49
'158': w5
'159': w50
'160': w55
'161': w56
'162': w57
'163': w58
'164': w59
'165': w60
'166': w62
'167': w63
'168': w66
'169': w8
'170': wo
'171': i6
'172': i7
'173': i8
'174': i9
'175': ilx
'176': p29
'177': w29
'178': w33
'179': w36
'180': w39
'181': w4
'182': w40
'183': w51
'184': w52
'185': w53
'186': w54
'187': w6
'188': w61
'189': w64
'190': w65
'191': w67
'192': w7
'193': w9
'194': pax
'195': pd
'196': pe
'197': phx
'198': plx
'199': pmx
'200': pnl
'201': prx
'202': pwx
'203': w11
'204': w14
'205': w15
'206': w17
'207': w19
'208': w2
'209': w23
'210': w25
'211': w26
'212': w27
'213': pl0
'214': pl4
'215': pl3
'216': pm2.5
'217': ph4.4
'218': pn40
'219': ph3.3
'220': ph2.6
- name: bbox
dtype:
- name: xmin
dtype: float32
- name: ymin
dtype: float32
- name: ymax
dtype: float32
- name: xmax
dtype: float32
splits:
- name: train
num_bytes: 1239201
num_examples: 6105
- name: validation
num_bytes: 196342
num_examples: 1097
- name: test
num_bytes: 617330
num_examples: 3071
download_size: 4761632373
dataset_size: 2052873
configs:
- config_name: default
data_files:
- split: train
path: default/train/data-*.arrow
- split: validation
path: default/validation/data-*.arrow
- split: test
path: default/test/data-*.arrow
Intro
The Tsinghua–Tencent 100K (TT100K) dataset is a large-scale traffic sign benchmark built from 100,000 Tencent Street View images, containing over 30,000 annotated instances across 221 categories. Designed for real-world detection and classification tasks, it features significant variation in lighting, weather, viewpoint, and distance. While the original study focuses on a 45-class subset using a 100-instance threshold per class, the Ultralytics configuration retains all 221 categories, including many with sparse samples, making the dataset both comprehensive and challenging for robust model development. The current dataset is a version from which negative samples have been removed.
Usage
from datasets import load_dataset
ds = load_dataset(
"Genius-Society/tt100k",
name="default",
split="train",
cache_dir="./__pycache__",
)
for i in ds:
print(i)
Maintenance
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/Genius-Society/tt100k
cd tt100k
Mirror
https://www.modelscope.cn/datasets/Genius-Society/tt100k