VisGym/inference-dataset

VLM-Gym Inference Dataset

This dataset contains pre-defined test episodes and initial states for evaluating Vision-Language Models (VLMs) on the VLM-Gym benchmark.

Dataset Structure

inference-dataset/
├── test_set_easy/           # Easy difficulty test episodes (JSONL)
├── test_set_hard/           # Hard difficulty test episodes (JSONL)
├── initial_states_easy/     # Initial environment states for easy episodes (JSON)
├── initial_states_hard/     # Initial environment states for hard episodes (JSON)
└── partial_datasets/        # Assets required by some environments
    ├── objaverse/           # 3D models for mental rotation tasks
    ├── counting/            # Images for counting tasks
    ├── refcoco+/            # Images for referring expression tasks
    └── ...

Tasks Included

Task	Description
maze_2d	2D maze navigation
maze_3d	3D maze navigation
mental_rotation_2d	2D shape rotation matching
mental_rotation_3d_cube	3D cube rotation matching
mental_rotation_3d_objaverse	3D object rotation matching
jigsaw	Jigsaw puzzle solving
sliding_block	Sliding block puzzle
colorization	Image colorization
counting	Object counting
patch_reassembly	Image patch reassembly
matchstick_equation	Matchstick equation solving
matchstick_rotation	Matchstick rotation
video_unshuffle	Video frame ordering
zoom_in_puzzle	Zoom-in puzzle solving
fetch_reach	Robotic reaching (easy only)
fetch_pick_and_place	Robotic manipulation (hard only)
referring_dot_pointing	Referring expression grounding (easy only)

Quick Start

Installation

pip install huggingface_hub

Download Full Dataset

from huggingface_hub import snapshot_download

dataset_path = snapshot_download(
    repo_id="VisGym/inference-dataset",
    repo_type="dataset",
)

Download Specific Subsets

from huggingface_hub import snapshot_download

# Download only test sets (small, no large assets)
dataset_path = snapshot_download(
    repo_id="VisGym/inference-dataset",
    repo_type="dataset",
    allow_patterns=["test_set_easy/**", "test_set_hard/**"],
)

# Download only easy difficulty
dataset_path = snapshot_download(
    repo_id="VisGym/inference-dataset",
    repo_type="dataset",
    allow_patterns=["*_easy/**"],
)

Using the Loader Script

# Download everything
python load_from_hf.py --output_dir ./inference_dataset

# Download only test sets (no large assets)
python load_from_hf.py --output_dir ./inference_dataset --subset test_sets

# Download only easy difficulty
python load_from_hf.py --output_dir ./inference_dataset --subset easy

File Formats

Test Set Files (JSONL)

Each line in the JSONL files contains an episode specification:

{"seed": 1803372, "env_id": "maze_2d/hard", "episode_seed": 1052368083, "extra_state": null}

Initial State Files (JSON)

JSON files containing the initial state for reproducible episode starts:

{
  "object_path": "000-156/fa3dad5169784cec85b96682231e3f44.glb",
  "secret_yaw": 1.098,
  "secret_pitch": 0.487,
  ...
}

Usage with VLM-Gym

from pathlib import Path
import json

# Load test episodes
test_file = Path(dataset_path) / "test_set_easy" / "maze_2d__easy" / "*.jsonl"
for jsonl_file in test_file.parent.glob("*.jsonl"):
    with open(jsonl_file) as f:
        for line in f:
            episode = json.loads(line)
            env_id = episode["env_id"]
            seed = episode["seed"]
            episode_seed = episode["episode_seed"]
            # Use with VLM-Gym inference runner

Citation

If you use this dataset, please cite:

@misc{vlmgym2024,
  title={VLM-Gym: A Benchmark for Vision-Language Models in Interactive Environments},
  author={VLM-Gym Team},
  year={2024},
  url={https://huggingface.co/datasets/VisGym/inference-dataset}
}

License

MIT License