Datasetpublic

mkxdxd/carla-dataset-ped2

CARLA Dataset — Pedestrian (Town05) · Extension 2 A large-scale pedestrian-following driving dataset captured from the CARLA simulator, all in Town05. Provides synchronized RGB + depth + camera parameters along each pedestrian trajectory. Part of the training data for the Seoul World Model. Stored in WebDataset (.tar) format for efficient streaming. Dataset at a glance Metric Value Town Town05 only Actor Pedestrian Frames / scene 200 Scenes /… See the full description on the dataset page: https://huggingface.co/datasets/mkxdxd/carla-dataset-ped2.

sourceHugging Facecc-by-4.0updated 3mo agoView on Hugging Face

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Dataset Card

CARLA Dataset — Pedestrian (Town05) · Extension 2

A large-scale pedestrian-following driving dataset captured from the CARLA simulator, all in Town05. Provides synchronized RGB + depth + camera parameters along each pedestrian trajectory. Part of the training data for the Seoul World Model. Stored in WebDataset (.tar) format for efficient streaming.

Dataset at a glance

Metric	Value
Town	Town05 only
Actor	Pedestrian
Frames / scene	200
Scenes / shard	2
Total scenes	~2,720
Total images	~544,000
Shards (`.tar`)	~1,365
Total size	~2.53 TB

Captured in four batches, stored as separate subfolders under Town05/:

Subfolder	Scenes	Shards
`pedestrian`	1,000	~500
`pedestrian2`	221	~111
`pedestrian3`	~700	~351
`pedestrian4`	799	~400

Repository structure

carla-dataset-ped2/
└── Town05/
    ├── pedestrian/
    │   ├── carla-stage2-000000.tar
    │   └── ...
    ├── pedestrian2/
    ├── pedestrian3/
    └── pedestrian4/

Each shard holds 2 complete scenes (200 frames each). Sample key: {scene_id}_{frame_idx:03d}.

Per-frame contents

File	Type	Description
`*.rgb.png`	`PIL.Image` `(1280×704)`	RGB image
`*.depth.npy`	`np.ndarray` `(704, 1280)`	Per-pixel depth map
`*.camera.json`	dict	`intrinsic`, `extrinsic`, `carla_transform`, `matched_references`
`*.metadata.json`	dict	`scene_id`, `frame_id`, `town`, `actor_type`

The matched_references IDs point into the shared reference pool (`references.tar`), so each target frame can be paired with its conditioning reference frames.

Usage

bash

pip install webdataset huggingface_hub numpy pillow

python

import io, json
import numpy as np
import webdataset as wds

url = ("https://huggingface.co/datasets/mkxdxd/carla-dataset-ped2/resolve/main/"
       "Town05/pedestrian/{carla-stage2-000000..carla-stage2-000010}.tar")

for s in wds.WebDataset(url).decode("pil"):
    rgb      = s["rgb.png"]                            # PIL.Image (1280×704)
    depth    = np.load(io.BytesIO(s["depth.npy"]))    # np.ndarray (704×1280)
    camera   = json.loads(s["camera.json"])
    metadata = json.loads(s["metadata.json"])
    print(s["__key__"], rgb.size, depth.shape, metadata["town"])
    break

Note: the Hugging Face dataset-viewer preview may error on this repo (Cannot write struct type 'target_to_reference_mapping' ...). This affects only the auto-generated Parquet preview — streaming via webdataset is unaffected.

Related datasets

—`mkxdxd/carla-dataset` — vehicle + pedestrian, Town01–06
—`mkxdxd/carla-dataset-ped` — pedestrian, 200-frame clips
—`kaistcvlab/carla-dataset-ped3` — Town05 pedestrian, extension 3
—`enrue1893/Synthetic-Ref-to-Urban-Video-Pairs-Town1-6` — reference ↔ target pairs

License

Released under CC-BY-4.0. Built using the CARLA simulator (MIT-licensed); see carla.org for simulator/asset terms.