---
license: cc-by-nc-4.0
task_categories:
  - image-classification
  - object-detection
language:
  - en
  - zh
tags:
  - dataset
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: float64
              - name: ymin
                dtype: float64         
              - name: ymax
                dtype: float64    
              - name: xmax
                dtype: float64
    splits:
      - name: train
        num_bytes: 1839408
        num_examples: 6105
      - name: validation
        num_bytes: 277496
        num_examples: 1097
      - name: test
        num_bytes: 917275
        num_examples: 3071
    download_size: 19152969603
    dataset_size: 3034179
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.

## Usage
```python
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
```bash
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>

## Thanks
 - <https://docs.ultralytics.com/datasets/detect/tt100k>