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README.md

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+---
+license: mit
+task_categories:
+  - visual-question-answering
+  - reinforcement-learning
+language:
+  - en
+tags:
+  - vlm
+  - gymnasium
+  - benchmark
+  - multimodal
+size_categories:
+  - 1G<n<10G
+---
+
+# 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
+
+```bash
+pip install huggingface_hub
+```
+
+### Download Full Dataset
+
+```python
+from huggingface_hub import snapshot_download
+
+dataset_path = snapshot_download(
+    repo_id="VisGym/inference-dataset",
+    repo_type="dataset",
+)
+```
+
+### Download Specific Subsets
+
+```python
+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
+
+```bash
+# 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:
+
+```json
+{"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:
+
+```json
+{
+  "object_path": "000-156/fa3dad5169784cec85b96682231e3f44.glb",
+  "secret_yaw": 1.098,
+  "secret_pitch": 0.487,
+  ...
+}
+```
+
+## Usage with VLM-Gym
+
+```python
+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:
+
+```bibtex
+@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