Link to this sectionTT100K Dataset#
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.
Link to this sectionKey Features#
- Multi-class detection: 221 traffic-sign categories covering Chinese speed-limit, prohibitory, warning, height/width-limit, and informative signs.
- High resolution: 2048×2048-pixel images, so signs range from large close-ups to tiny distant markers that stress fine-grained detection.
- Real-world conditions: Large variations in weather, illumination, viewing angle, and occlusion.
- Bounding-box annotations: Each sign is labeled with a class and a bounding box in YOLO format after automatic conversion.
- Pre-defined splits: Fixed train / val / test splits (6,105 / 7,641 / 3,071 images) for consistent evaluation.
Link to this sectionDataset Structure#
The Ultralytics TT100K configuration is split into three subsets, all sharing the same 221 categories:
| Split | Images | Description |
|---|---|---|
| Train | 6,105 | Labeled traffic-scene images used to train the detector |
| Validation | 7,641 | The dataset's original "other" split, used for evaluation |
| Test | 3,071 | Held-out images for final evaluation of the trained model |
The 221 categories are organized into several major groups:
- 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.
Link to this sectionApplications#
TT100K is widely used for building and benchmarking traffic-sign recognition in real-world conditions. Common applications include:
- Autonomous driving perception systems
- Advanced driver-assistance systems (ADAS)
- Traffic monitoring and road-infrastructure analysis
- Urban planning and traffic-flow studies
- Computer vision research on small objects in high-resolution imagery
Link to this sectionDataset 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)Link to this sectionUsage#
TT100K downloads automatically the first time you train and requires about 18 GB of free disk space. On first use the download script fetches the original data and converts the annotations to YOLO format, which can take several minutes.
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.
Link to this sectionSample Data and Annotations#
TT100K images are 2048×2048 street scenes in which traffic signs often occupy only a small fraction of the frame. A single image can contain multiple signs at different scales and distances, some partially occluded by vehicles, vegetation, or structures, and captured under day/night and clear/rainy conditions. This mix of small objects and challenging conditions is what makes the dataset a strong test of detector robustness.
Link to this sectionCitations and Acknowledgments#
If you use the TT100K dataset in your research or development work, please cite the following paper:
@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.
Link to this sectionFAQ#
Link to this sectionWhat is the TT100K dataset used for?#
The Tsinghua-Tencent 100K (TT100K) dataset is used for traffic-sign detection and classification in real-world conditions. Its 221 categories and high-resolution street-view imagery make it a common benchmark for autonomous-driving perception, advanced driver-assistance systems (ADAS), and small-object detection research.
Link to this sectionHow many images are in the TT100K dataset?#
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.
Link to this sectionWhy is it called 100K if there are about 16,800 images?#
The "100K" refers to the roughly 100,000 Tencent Street View images the original benchmark was created from. The Ultralytics detection configuration provides 16,817 of those images with YOLO-format labels; the name reflects the source collection, not the training-set size.
Link to this sectionHow many traffic-sign categories are in TT100K?#
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.
Link to this sectionHow big is the TT100K dataset download?#
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.
Link to this sectionHow do I train a YOLO26 model on the TT100K dataset?#
Train a YOLO26n model on TT100K for 100 epochs at an image size of 640:
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.
Link to this sectionHow do I handle the large 2048×2048 images in TT100K?#
Start with imgsz=640 for initial experiments, then raise to imgsz=1280 (with a smaller batch) 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.