---
title: Argoverse Detection Dataset
comments: true
creator:
    name: Argo AI
    url: https://www.argoverse.org/av1.html
license:
    name: CC-BY-NC-SA-4.0
description: Train YOLO object detection models on the Argoverse (Argoverse-HD) dataset — 54,446 autonomous-driving images across 8 classes, from a ring-front-center camera.
keywords: Argoverse dataset, Argoverse-HD, object detection, 2D detection, autonomous driving, self-driving car dataset, YOLO26, traffic detection, Ultralytics
---

# Argoverse Dataset

The Ultralytics Argoverse dataset (Argoverse-HD) is a 2D [object detection](../../tasks/detect.md) 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](https://www.argoverse.org/)'s Argoverse 1.1 driving data. It is a large, real-world benchmark for training [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models to detect road objects in self-driving scenarios.

!!! note "Manual download required"

    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](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link) — 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](https://eval.ai/web/challenges/challenge-page/800/overview).
- **~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](https://eval.ai/web/challenges/challenge-page/800/overview)) |

All images share the same 8 object classes (indices 0–7): person, bicycle, car, motorcycle, bus, truck, traffic light, and stop sign.

!!! tip "Automatic YOLO conversion"

    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](../../tasks/detect.md) applications in autonomous driving:

- **Self-driving perception** — detect vehicles, pedestrians, and cyclists from a forward-facing camera to support [autonomous-vehicle](https://www.ultralytics.com/solutions/ai-in-automotive) 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](../../modes/train.md) and [prediction](../../modes/predict.md) 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](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml).

!!! example "ultralytics/cfg/datasets/Argoverse.yaml"

    ```yaml
    --8<-- "ultralytics/cfg/datasets/Argoverse.yaml"
    ```

## Usage

To train a YOLO26n model on the Argoverse dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following code samples. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.

!!! example "Train Example"

    === "Python"

        ```python
        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)
        ```

    === "CLI"

        ```bash
        # Start training from a pretrained *.pt model
        yolo detect train data=Argoverse.yaml model=yolo26n.pt epochs=100 imgsz=640
        ```

Once trained, run [inference](../../modes/predict.md) with the fine-tuned model on new driving images or video:

!!! example "Inference Example"

    === "Python"

        ```python
        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")
        ```

    === "CLI"

        ```bash
        # Start prediction with a finetuned *.pt model
        yolo detect predict model='path/to/best.pt' imgsz=640 source="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:

![Argoverse-HD autonomous driving scene with annotated road objects](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/argoverse-3d-tracking-sample.avif)

- **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:

!!! quote ""

    === "BibTeX"

        ```bibtex
        @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](https://www.argoverse.org/) for creating the original Argoverse dataset as a valuable resource for the autonomous-driving research community.

## FAQ

### What is the Argoverse dataset, and what is it used for?

The Ultralytics Argoverse dataset (Argoverse-HD) is a 2D [object detection](../../tasks/detect.md) 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.

### How many classes and images are in the Argoverse dataset?

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.

### Is the Argoverse dataset 2D or 3D detection in Ultralytics?

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`.

### How do I train a YOLO26 model using the Argoverse dataset?

Download the dataset manually first (see below), then train with the `Argoverse.yaml` configuration file:

!!! example

    === "Python"

        ```python
        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)
        ```

    === "CLI"

        ```bash
        # Start training from a pretrained *.pt model
        yolo detect train data=Argoverse.yaml model=yolo26n.pt epochs=100 imgsz=640
        ```

For a detailed explanation of the arguments, refer to the model [Training](../../modes/train.md) page.

### Where can I download the Argoverse dataset now that it has been removed from Amazon S3?

The Argoverse-HD `*.zip` file (~31.5 GB), previously hosted on Amazon S3, can now be downloaded manually from [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link). Automatic download will not work, so fetch the archive before running your training command.

### Can I use the Argoverse dataset with Ultralytics Platform?

Yes. [Ultralytics Platform](https://platform.ultralytics.com) lets you upload and version large datasets like Argoverse-HD, then train and deploy [object detection](../../tasks/detect.md) models in the cloud without heavy local setup. You can also browse related datasets in the [detection datasets overview](index.md).
