README.md

metadata

license: apache-2.0
task_categories:
  - video-text-to-text
  - robotics
language:
  - en
tags:
  - video
  - reasoning
  - perception
  - embodied
  - simulation
  - webdataset
size_categories:
  - 100K<n<1M

VideoReason Training Dataset

A large-scale video reasoning training dataset spanning perception, simulation, and embodied tasks.

Dataset Overview

Subset	Samples	Description
perception	177,407	Visual perception tasks
simulation	105,818	3D scene navigation with camera motion sequences in simulated environments
embodied	188,845	Robotic manipulation tasks
Total	472,070

Each sample consists of a video (.mp4) paired with a text prompt describing the task.

Perception Breakdown

Task	Samples	Description
Segmentation	40,000	Instance segmentation with color-fill visualization
Denoising	32,093	Image denoising from noisy inputs
Low-light Enhancement	32,103	Enhancing images captured in low-light conditions
Super-resolution	32,090	Single-image super-resolution
Edge Detection	30,000	Boundary and edge extraction
Keypoint Detection	11,121	Detecting structural keypoints

Embodied Breakdown

Source	Samples	Description
DROID	94,237	Real-world robotic manipulation from the DROID dataset
RoboTwin	94,608	Simulated bimanual robotic manipulation from RoboTwin

Repository Structure

README.md
{subset}/prompts.jsonl          # Metadata: one JSON object per line
{subset}/shards/shard-NNNNNN.tar  # WebDataset tar shards (~1GB each)

prompts.jsonl format

{"video_path": "video/seg/example.mp4", "prompt": "Identify a dining table in this image..."}

• video_path: original relative path of the video within the subset
• prompt: text prompt associated with the video

Tar shard contents

Each shard is a standard .tar archive containing paired files:

000000.mp4    # video file
000000.json   # {"video_path": "...", "prompt": "..."}
000001.mp4
000001.json
...

The 6-digit key is the global sample index within the subset (matching the line number in prompts.jsonl, 0-indexed).

Download

From ModelScope

pip install modelscope

# Download entire dataset
modelscope download --dataset ZaneQiu/XVReason --local_dir videoreason-training

# Download a specific subset
modelscope download --dataset ZaneQiu/XVReason --local_dir videoreason-training --include "perception/*"

From HuggingFace

pip install huggingface_hub

huggingface-cli download Zane-QIU/videoreason-training --repo-type dataset --local-dir videoreason-training

Extracting Tar Shards

Option 1: Extract all shards to a directory

# Extract all shards for a subset
mkdir -p extracted/perception
for f in videoreason-training/perception/shards/shard-*.tar; do
    tar xf "$f" -C extracted/perception
done

Option 2: Use WebDataset for streaming (recommended for training)

import webdataset as wds

dataset = wds.WebDataset("videoreason-training/perception/shards/shard-{000000..000028}.tar")
for sample in dataset:
    video_bytes = sample[".mp4"]
    metadata = json.loads(sample[".json"])
    print(metadata["prompt"])

Option 3: Extract a single shard

tar xf perception/shards/shard-000000.tar -C output_dir/

License

Please refer to the original dataset licenses for each subset:

• DROID: DROID Dataset
• RoboTwin: RoboTwin