TT100K データセット#
The Tsinghua-Tencent 100K (TT100K) dataset is a traffic-sign benchmark for object detection, created by Zhu et al. for the CVPR 2016 paper Traffic-Sign Detection and Classification in the Wild. The Ultralytics TT100K configuration provides 16,817 images (6,105 train / 7,641 val / 3,071 test) across 221 traffic-sign categories, with the source benchmark reporting over 30,000 sign instances. The "100K" in the name refers to the roughly 100,000 Tencent Street View images the benchmark was created from — not the number of images you download and train on. Because the original paper keeps only categories with at least 100 training instances, a commonly used 45-class subset exists, but the Ultralytics configuration retains all 221 annotated categories (many of them sparse).
The high-resolution street-view imagery captures large variations in illumination, weather, viewing angle, and distance, making TT100K a demanding benchmark for small-object detection in real-world driving scenes.
主な特徴#
- マルチクラス検出: 中国の速度制限、禁止、警告、高さ/幅制限、および情報標識を網羅する221の交通標識カテゴリ。
- 高解像度: 2048×2048ピクセルの画像であり、大きなクローズアップから微細な検出を必要とする遠方の小さなマーカーまで、標識の範囲が広がります。
- 現実の条件: 天候、照明、視野角、遮蔽における大きな変化。
- Bounding-box annotations: Each sign is labeled with a class and a bounding box in YOLO format after automatic conversion.
- 定義済みスプリット: 一貫した評価のための固定された学習/検証/テストスプリット（6,105 / 7,641 / 3,071枚の画像）。
データセットの構造#
Ultralytics TT100K構成は3つのサブセットに分割されており、すべて同じ221カテゴリを共有しています。
| 分割 | 画像 | 説明 |
|---|---|---|
| トレーニング | 6,105 | 検出器の学習に使用されるラベル付き交通シーン画像 |
| バリデーション | 7,641 | The dataset's original "other" split, used for evaluation |
| テスト | 3,071 | 学習済みモデルの最終評価のために保持された画像 |
221のカテゴリは、いくつかの主要なグループに整理されています。
- Speed-limit signs — prohibitory limits
pl*(e.g., pl5–pl120) and minimum speedspm*(e.g., pm5–pm55). - Prohibitory signs — general prohibitions
p1–p29, no-entry/no-parkingpn/pne, and restrictionspr*(pr10–pr100). - Warning signs —
w1–w67for road hazards such as crossings, sharp turns, slippery roads, and construction. - Height/width-limit signs — height limits
ph*(e.g., ph2–ph5.5) and width limitspb/pw*. - Informative signs — general info
i1–i15, speed-limit infoil*(il50–il110), otherio, and information platesip.
アプリケーション#
TT100Kは、現実の条件下での交通標識認識の構築およびベンチマーク作成に広く使用されています。一般的なアプリケーションには以下が含まれます。
- 自動運転認識システム
- 先進運転支援システム (ADAS)
- 交通監視および道路インフラ分析
- 都市計画および交通流研究
- Computer vision research on small objects in high-resolution imagery
データセット YAML#
The TT100K.yaml file defines the dataset configuration — the dataset paths, class names, and the automatic download-and-convert script. It is maintained in the Ultralytics repository at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/TT100K.yaml.
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Tsinghua-Tencent 100K (TT100K) dataset https://cg.cs.tsinghua.edu.cn/traffic-sign/ by Tsinghua University
# Documentation: https://cg.cs.tsinghua.edu.cn/traffic-sign/tutorial.html
# Paper: Traffic-Sign Detection and Classification in the Wild (CVPR 2016)
# License: CC BY-NC 2.0 license for non-commercial use only
# Example usage: yolo train data=TT100K.yaml
# parent
# ├── ultralytics
# └── datasets
# └── TT100K ← downloads here (~18 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: TT100K # dataset root dir
train: images/train # train images (relative to 'path') 6105 images
val: images/val # val images (relative to 'path') 7641 images (original 'other' split)
test: images/test # test images (relative to 'path') 3071 images
# Classes (221 traffic sign categories, 45 with sufficient training instances)
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
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
import json
import shutil
from pathlib import Path
from PIL import Image
from ultralytics.utils import TQDM
from ultralytics.utils.downloads import download
def tt100k2yolo(dir):
"""Convert TT100K annotations to YOLO format with images/{split} and labels/{split} structure."""
data_dir = dir / "data"
anno_file = data_dir / "annotations.json"
print("Loading annotations...")
with open(anno_file, encoding="utf-8") as f:
data = json.load(f)
# Build class name to index mapping from yaml
names = yaml["names"]
class_to_idx = {v: k for k, v in names.items()}
# Create directories
for split in ["train", "val", "test"]:
(dir / "images" / split).mkdir(parents=True, exist_ok=True)
(dir / "labels" / split).mkdir(parents=True, exist_ok=True)
print("Converting annotations to YOLO format...")
skipped = 0
for img_id, img_data in TQDM(data["imgs"].items(), desc="Processing"):
img_path_str = img_data["path"]
if "train" in img_path_str:
split = "train"
elif "test" in img_path_str:
split = "test"
else:
split = "val"
# Source and destination paths
src_img = data_dir / img_path_str
if not src_img.exists():
continue
dst_img = dir / "images" / split / src_img.name
# Get image dimensions
try:
with Image.open(src_img) as img:
img_width, img_height = img.size
except Exception as e:
print(f"Error reading {src_img}: {e}")
continue
# Copy image to destination
shutil.copy2(src_img, dst_img)
# Convert annotations
label_file = dir / "labels" / split / f"{src_img.stem}.txt"
lines = []
for obj in img_data.get("objects", []):
category = obj["category"]
if category not in class_to_idx:
skipped += 1
continue
bbox = obj["bbox"]
xmin, ymin = bbox["xmin"], bbox["ymin"]
xmax, ymax = bbox["xmax"], bbox["ymax"]
# Convert to YOLO format (normalized center coordinates and dimensions)
x_center = ((xmin + xmax) / 2.0) / img_width
y_center = ((ymin + ymax) / 2.0) / img_height
width = (xmax - xmin) / img_width
height = (ymax - ymin) / img_height
# Clip to valid range
x_center = max(0, min(1, x_center))
y_center = max(0, min(1, y_center))
width = max(0, min(1, width))
height = max(0, min(1, height))
cls_idx = class_to_idx[category]
lines.append(f"{cls_idx} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}\n")
# Write label file
if lines:
label_file.write_text("".join(lines), encoding="utf-8")
if skipped:
print(f"Skipped {skipped} annotations with unknown categories")
print("Conversion complete!")
# Download
dir = Path(yaml["path"]) # dataset root dir
urls = ["https://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/data.zip"]
download(urls, dir=dir, curl=True, threads=1)
# Convert
tt100k2yolo(dir)使用方法#
TT100Kは、初めて学習を実行する際に自動的にダウンロードされ、約18 GBの空きディスク容量が必要です。初回使用時には、ダウンロードスクリプトが元のデータを取得し、注釈をYOLO形式に変換します。これには数分かかる場合があります。
To train a YOLO26n model on the TT100K dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
# Train the model - dataset will auto-download on first run
results = model.train(data="TT100K.yaml", epochs=100, imgsz=640)To label additional traffic-sign images and manage TT100K training runs in your browser, use Ultralytics Platform.
サンプルデータとアノテーション#
TT100Kの画像は、交通標識がフレームのほんの一部しか占めない2048×2048のストリートシーンです。1枚の画像には、異なるスケールや距離で複数の標識が含まれることがあり、一部は車両、植生、または構造物によって部分的に隠れていたり、昼夜や晴れ/雨といった条件下で撮影されていたりします。この小物体の混在と挑戦的な条件が、このデータセットを検出器の堅牢性を試す強力なテストにしています。
引用と謝辞#
研究や開発作業で TT100K データセットを使用する場合は、以下の論文を引用してください。
@InProceedings{Zhu_2016_CVPR,
author = {Zhu, Zhe and Liang, Dun and Zhang, Songhai and Huang, Xiaolei and Li, Baoli and Hu, Shimin},
title = {Traffic-Sign Detection and Classification in the Wild},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}We would like to acknowledge the Tsinghua University and Tencent collaboration for creating and maintaining this valuable resource for the computer vision and autonomous driving communities. For more information about the TT100K dataset, visit the official dataset website.
よくある質問 (FAQ)#
Tsinghua-Tencent 100K (TT100K) データセットは、現実の条件下での交通標識の検出および分類に使用されます。その221のカテゴリと高解像度のストリートビュー画像は、自動運転認識、先進運転支援システム (ADAS)、および小物体検出研究のための共通ベンチマークとなっています。
The Ultralytics TT100K configuration contains 16,817 images: 6,105 for training, 7,641 for validation (the dataset's original "other" split), and 3,071 for testing. See the Dataset Structure section for the full breakdown.
「100K」は、元のベンチマークの作成元となった約10万枚のTencentストリートビュー画像を指しています。Ultralytics検出構成では、そのうち16,817枚がYOLO形式のラベル付きで提供されており、その名称は学習セットのサイズではなく、ソースコレクションを反映しています。
TT100K defines 221 categories spanning speed-limit, prohibitory, warning, height/width-limit, and informative signs. The original paper keeps only the 45 categories with at least 100 training instances, but the Ultralytics configuration retains all 221. See Dataset Structure for the group breakdown.
TT100K is about 18 GB and downloads automatically the first time you train with
data="TT100K.yaml"— no manual download is required. The script also converts the original annotations to YOLO format on first run.TT100KでYOLO26nモデルを画像サイズ640で100エポック学習します：
学習例from ultralytics import YOLO # Load a model model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training) # Train the model results = model.train(data="TT100K.yaml", epochs=100, imgsz=640)For detailed configurations, see the Training page and model training tips.
Start with
imgsz=640for initial experiments, then raise toimgsz=1280(with a smallerbatch) if you have enough GPU memory, since the higher resolution helps recover small, distant signs:model.train(data="TT100K.yaml", imgsz=1280, batch=4) # higher resolution for small signsYou can also tune data augmentation and consider tiling strategies for very small objects. See Tips for Model Training for more.