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---
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](https://huggingface.co/datasets/TESS-Computer/atari-vla-stage1-15hz) |
| 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", 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 \
--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) - Original with 160x210 images
- [Raw 5Hz dataset](https://huggingface.co/datasets/TESS-Computer/atari-vla-stage1-5hz) - Single action per observation
- [TESS-Atari repo](https://github.com/HusseinLezzaik/TESS-Atari) - Training code
## Citation
```bibtex
@misc{tessatari2025,
title={TESS-Atari: Vision-Language-Action Models for Atari Games},
author={Lezzaik, Hussein},
year={2025},
url={https://github.com/HusseinLezzaik/TESS-Atari}
}
@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}
}
```
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