| --- |
| dataset_info: |
| features: |
| - name: image_bytes |
| dtype: binary |
| - name: action |
| dtype: string |
| - name: game |
| dtype: string |
| - name: trial_id |
| dtype: int32 |
| - name: frame_idx |
| dtype: int32 |
| - name: image_size |
| dtype: int32 |
| license: mit |
| task_categories: |
| - robotics |
| - reinforcement-learning |
| tags: |
| - atari |
| - vla |
| - vision-language-action |
| - imitation-learning |
| - preprocessed |
| - smolvlm |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # TESS-Atari Stage 1 - Preprocessed (15Hz, 384x384) |
|
|
| **Training-ready** version of the 15Hz dataset with images pre-resized to 384x384 (SmolVLM native resolution). |
|
|
| ## Overview |
|
|
| | Metric | Value | |
| |--------|-------| |
| | Source | [TESS-Computer/atari-vla-stage1-15hz-clean](https://huggingface.co/datasets/TESS-Computer/atari-vla-stage1-15hz-clean) | |
| | Samples | 1,340,293 | |
| | Image Size | 384x384 (pre-resized) | |
| | Action Rate | 15 Hz (3 actions per observation) | |
| | Format | Lumine-style action tokens | |
|
|
| ## Why Preprocessed? |
|
|
| Training VLMs requires resizing images to the model's native resolution. Doing this on-the-fly creates a CPU bottleneck. This dataset has images **already resized**, giving ~10x faster training: |
|
|
| ``` |
| Raw dataset: 160x210 → resize during training → slow (CPU bound) |
| Preprocessed: 384x384 → ready to use → fast (GPU saturated) |
| ``` |
|
|
| ## Action Format |
|
|
| ``` |
| <|action_start|> RIGHT ; RIGHT ; FIRE <|action_end|> |
| <|action_start|> LEFT ; LEFT ; LEFT <|action_end|> |
| <|action_start|> NOOP ; UP ; UPFIRE <|action_end|> |
| ``` |
|
|
| ## Schema |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `image_bytes` | bytes | PNG at 384x384 (pre-resized) | |
| | `action` | string | Lumine-style chunked action token | |
| | `game` | string | Game name | |
| | `trial_id` | int | Human player trial number | |
| | `frame_idx` | int | Frame index in trial | |
| | `image_size` | int | Always 384 | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| from PIL import Image |
| from io import BytesIO |
| |
| # Load preprocessed dataset |
| ds = load_dataset("TESS-Computer/tess-atari-15hz-384-clean", split="train") |
| |
| # Images are already 384x384 - no resizing needed! |
| sample = ds[0] |
| img = Image.open(BytesIO(sample["image_bytes"])) |
| print(img.size) # (384, 384) |
| print(sample["action"]) # <|action_start|> LEFT ; LEFT ; LEFT <|action_end|> |
| ``` |
|
|
| ## Training |
|
|
| ```bash |
| python scripts/train_v2.py \ |
| --preprocessed TESS-Computer/tess-atari-15hz-384-clean \ |
| --epochs 3 \ |
| --batch-size 4 \ |
| --grad-accum 32 \ |
| --wandb \ |
| --push-to-hub |
| ``` |
|
|
| ## Related |
|
|
| - [Raw 15Hz dataset](https://huggingface.co/datasets/TESS-Computer/atari-vla-stage1-15hz-clean) - Original with 160x210 images |
| - [Raw 5Hz dataset](https://huggingface.co/datasets/TESS-Computer/atari-vla-stage1-5hz-clean) - Single action per observation |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{atarihead2019, |
| title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset}, |
| author={Zhang, Ruohan and others}, |
| year={2019}, |
| url={https://zenodo.org/records/3451402} |
| } |
| ``` |
|
|
