Argoverse Dataset#
The Ultralytics Argoverse dataset (Argoverse-HD) is a 2D object detection dataset of 54,446 labeled autonomous-driving images — 39,384 for training and 15,062 for validation — across 8 classes: person, bicycle, car, motorcycle, bus, truck, traffic light, and stop sign. The images are captured from a vehicle's ring-front-center camera, and the annotations come from Carnegie Mellon University's streaming-perception project, built on Argo AI's Argoverse 1.1 driving data. It is a large, real-world benchmark for training computer vision models to detect road objects in self-driving scenarios.
The Argoverse-HD *.zip file (~31.5 GB) needed for training was removed from Amazon S3 after the shutdown of Argo AI by Ford. It is available for manual download from Google Drive — automatic download will not work, so download the archive before training.
Key Features#
- 8 object-detection classes: person, bicycle, car, motorcycle, bus, truck, traffic light, and stop sign.
- 54,446 labeled images — 39,384 for training and 15,062 for validation — plus an unlabeled test split reserved for the eval.ai challenge.
- ~31.5 GB of high-resolution ring-front-center camera frames captured in urban autonomous-driving scenes.
- Annotations are converted to YOLO format automatically on first use, so the dataset trains directly with Ultralytics YOLO detection models.
Dataset Structure#
The Argoverse-HD dataset is split into three predefined subsets, defined by the Argoverse.yaml configuration:
| Split | Images | Labels |
|---|---|---|
| Train | 39,384 | Yes |
| Validation | 15,062 | Yes |
| Test | — | Unlabeled (eval.ai challenge) |
All images share the same 8 object classes (indices 0–7): person, bicycle, car, motorcycle, bus, truck, traffic light, and stop sign.
After the manual download, Ultralytics converts the original Argoverse-HD annotations into YOLO detection labels automatically the first time you train, so no manual preprocessing is required.
Applications#
The Argoverse-HD dataset supports a range of object detection applications in autonomous driving:
- Self-driving perception — detect vehicles, pedestrians, and cyclists from a forward-facing camera to support autonomous-vehicle navigation.
- Advanced driver-assistance systems (ADAS) — recognize traffic lights and stop signs for real-time driver alerts.
- Traffic monitoring — count and track road users in urban scenes for smart-city analytics.
- Research and prototyping — a large, real-world benchmark for learning model training and prediction on driving data.
Dataset YAML#
A YAML file defines the dataset configuration, including paths, classes, and other relevant details. For the Argoverse dataset, the Argoverse.yaml file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml.
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Argoverse-HD dataset (ring-front-center camera) by Argo AI: https://www.cs.cmu.edu/~mengtial/proj/streaming/
# Documentation: https://docs.ultralytics.com/datasets/detect/argoverse
# Example usage: yolo train data=Argoverse.yaml
# parent
# ├── ultralytics
# └── datasets
# └── Argoverse ← downloads here (31.5 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: Argoverse # dataset root dir
train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
# Classes
names:
0: person
1: bicycle
2: car
3: motorcycle
4: bus
5: truck
6: traffic_light
7: stop_sign
# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
import json
from pathlib import Path
from ultralytics.utils import TQDM
from ultralytics.utils.downloads import download
def argoverse2yolo(annotation_file):
"""Convert Argoverse dataset annotations to YOLO format for object detection tasks."""
labels = {}
with open(annotation_file, encoding="utf-8") as f:
a = json.load(f)
for annot in TQDM(a["annotations"], desc=f"Converting {annotation_file} to YOLO format..."):
img_id = annot["image_id"]
img_name = a["images"][img_id]["name"]
img_label_name = f"{Path(img_name).stem}.txt"
cls = annot["category_id"] # instance class id
x_center, y_center, width, height = annot["bbox"]
x_center = (x_center + width / 2) / 1920.0 # offset and scale
y_center = (y_center + height / 2) / 1200.0 # offset and scale
width /= 1920.0 # scale
height /= 1200.0 # scale
img_dir = annotation_file.parents[2] / "Argoverse-1.1" / "labels" / a["seq_dirs"][a["images"][annot["image_id"]]["sid"]]
if not img_dir.exists():
img_dir.mkdir(parents=True, exist_ok=True)
k = str(img_dir / img_label_name)
if k not in labels:
labels[k] = []
labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
for k in labels:
with open(k, "w", encoding="utf-8") as f:
f.writelines(labels[k])
# Download 'https://argoverse-hd.s3.amazonaws.com/Argoverse-HD-Full.zip' (deprecated S3 link)
dir = Path(yaml["path"]) # dataset root dir
urls = ["https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link"]
print("\n\nWARNING: Argoverse dataset MUST be downloaded manually, autodownload will NOT work.")
print(f"WARNING: Manually download Argoverse dataset '{urls[0]}' to '{dir}' and re-run your command.\n\n")
# download(urls, dir=dir)
# Convert
annotations_dir = "Argoverse-HD/annotations/"
(dir / "Argoverse-1.1" / "tracking").rename(dir / "Argoverse-1.1" / "images") # rename 'tracking' to 'images'
for d in "train.json", "val.json":
argoverse2yolo(dir / annotations_dir / d) # convert Argoverse annotations to YOLO labelsUsage#
To train a YOLO26n model on the Argoverse dataset for 100 epochs with an image size of 640, use the following code samples. 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
results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)Once trained, run inference with the fine-tuned model on new driving images or video:
from ultralytics import YOLO
# Load a model
model = YOLO("path/to/best.pt") # load an Argoverse fine-tuned model
# Inference using the model
results = model.predict("path/to/driving-scene.jpg")Sample Data and Annotations#
The Argoverse-HD dataset contains high-resolution driving images captured from a ring-front-center camera, annotated with 2D bounding boxes for the 8 object classes. Below is an example image from the dataset with its corresponding annotations:
- Annotated driving scene: This image shows road objects — such as vehicles and pedestrians — labeled with 2D bounding boxes, the format YOLO models learn to predict during training.
Citations and Acknowledgments#
The Argoverse-HD 2D detection annotations used in this dataset come from Carnegie Mellon University's streaming-perception work. If you use the dataset in your research or development, please cite:
@inproceedings{li2020towards,
title={Towards Streaming Perception},
author={Li, Mengtian and Wang, Yu-Xiong and Ramanan, Deva},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={473--488},
year={2020}
}
@inproceedings{chang2019argoverse,
title={Argoverse: 3D Tracking and Forecasting with Rich Maps},
author={Chang, Ming-Fang and Lambert, John and Sangkloy, Patsorn and Singh, Jagjeet and Bak, Slawomir and Hartnett, Andrew and Wang, Dequan and Carr, Peter and Lucey, Simon and Ramanan, Deva and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={8748--8757},
year={2019}
}We would like to acknowledge Carnegie Mellon University for the Argoverse-HD detection annotations and Argo AI for creating the original Argoverse dataset as a valuable resource for the autonomous-driving research community.
FAQ#
The Ultralytics Argoverse dataset (Argoverse-HD) is a 2D object detection dataset of 54,446 autonomous-driving images across 8 classes — person, bicycle, car, motorcycle, bus, truck, traffic light, and stop sign. It is used to train and evaluate models that detect road objects from a forward-facing vehicle camera, supporting self-driving perception, ADAS, and traffic-monitoring research.
The Argoverse-HD dataset has 8 classes (person, bicycle, car, motorcycle, bus, truck, traffic light, and stop sign) and 54,446 labeled images — 39,384 for training and 15,062 for validation — plus an unlabeled test split reserved for the eval.ai challenge.
In Ultralytics it is a 2D object detection dataset (Argoverse-HD camera frames with 2D bounding boxes), not the 3D-tracking, motion-forecasting, or LiDAR research suite from the broader Argoverse program. You train it with a standard detection model such as
yolo26n.pt.Download the dataset manually first (see below), then train with the
Argoverse.yamlconfiguration file:Examplefrom ultralytics import YOLO # Load a model model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training) # Train the model results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)For a detailed explanation of the arguments, refer to the model Training page.
The Argoverse-HD
*.zipfile (~31.5 GB), previously hosted on Amazon S3, can now be downloaded manually from Google Drive. Automatic download will not work, so fetch the archive before running your training command.Yes. Ultralytics Platform lets you upload and version large datasets like Argoverse-HD, then train and deploy object detection models in the cloud without heavy local setup. You can also browse related datasets in the detection datasets overview.