Molmo2-VideoTrackEval

Molmo2-VideoTrackEval is an evaluation benchmark for video point tracking, containing human-annotated ground truth expressions. It includes segmentation masks for evaluating whether predicted points fall within the correct object regions. Currently, there are five categories for evaluation:

• animal
• dance
• sports
• person
• misc

This benchmark is part of the Molmo2 dataset collection and is used to evaluate the Molmo2 family of models on video object tracking via point trajectories.

Quick links:

• 📃 Paper
• 🎥 Blog with Videos

Usage

from datasets import load_dataset

# Load entire evaluation dataset
ds = load_dataset("allenai/Molmo2-VideoTrackEval", split="test")

# Load a specific benchmark subset by config name
animal = load_dataset("allenai/Molmo2-VideoTrackEval", "animal", split="test")
dance = load_dataset("allenai/Molmo2-VideoTrackEval", "dance", split="test")
sports = load_dataset("allenai/Molmo2-VideoTrackEval", "sports", split="test")
person = load_dataset("allenai/Molmo2-VideoTrackEval", "person", split="test")
misc = load_dataset("allenai/Molmo2-VideoTrackEval", "misc", split="test")

Available Configs

Config	Dataset	Description
default	All	All evaluation data combined
animal	APTv2	Animal tracking benchmark
dance	dancetrack	Dancer tracking benchmark
sports	sportsmot	Sports player tracking benchmark
person	personpath22	Person tracking benchmark
misc	sav	Misc Video benchmark

Data Format

Each row contains tracking annotations for one or more objects in a video clip:

Field	Description
id	Unique identifier for this annotation
video	Video filename
clip	trimmed clip id
video_dataset	Source dataset name (e.g., 'dancetrack', 'sportsmot')
video_source	Video directory path (can be ignored)
exp	Text expression describing the tracked object(s)
obj_id	List of object IDs per video
mask_id	List of mask IDs corresponding to tracked objects starting from '0'
masks	List of segmentation masks per object for evaluation. Each entry contains object_id and masks (used to verify if predicted points fall within the ground truth object region)
points	List of point trajectories per object. Each entry contains object_id and points (list of [x, y] coordinates per frame)
segments	List of segment annotations per object. Each entry contains object_id and segments
start_frame	Starting frame index for this clip
end_frame	Ending frame index for this clip
w	Video width
h	Video height
n_frames	Number of frames in the clip
fps	Frames per second

Important: start_frame and end_frame indicate which portion of the source video to use. You need to trim the video to this range — the annotations correspond to frames within [start_frame, end_frame], not the entire video.

Evaluation with Masks

The masks field contains ground truth segmentation masks that can be used to evaluate tracking predictions. A predicted point is considered correct if it falls within the segmentation mask of the target object for that frame.

Folder Structure

Molmo2-VideoTrackEval/
├── README.md
└── data/
    ├── animal/
    │   └── APTv2_point_tracks_with_masks.parquet
    ├── dance/
    │   └── dancetrack_point_tracks_with_masks.parquet
    └── sports/
        └── sportsmot_point_tracks_with_masks.parquet
    ├── person/
    │   └── personpath22_point_tracks_with_masks.parquet
    ├── misc/
    │   └── sav_point_tracks_with_masks.parquet

Video Sources

The table below contains information on the sources of the third party datasets used or referenced in curating the benchmark data for Molmo2-VideoTrackEval. We do not provide video files or share the original raw data from datasets with restrictions on use and distribution according to the source license.

Dataset	Category	Download	Dataset License
APTv2	Animals	APTv2	Apache 2.0
dancetrack	Dancers	DanceTrack	Non-commercial research use only
sportsmot	Sports	SportsMOT	CC BY-NC 4.0
personpath22	Person	PersonPath22	CC BY-NC 4.0
sav	Misc	SA-V (Frames sampled at 6 fps from 24 fps video)	CC BY 4.0

License

This dataset is licensed under ODC-BY-1.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines. Please refer to the Video Sources section for the original datasets that provide the videos used to generate the segmentations and point tracks for this dataset. All use of the videos and original data from these datasets are subject to the licenses and terms of use provided by the sources. Please check the sources to determine if they are appropriate for your use case.