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README.md

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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{atarihead2019,
+  title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset},
+  author={Zhang, Ruohan and others},
+  year={2019},
+  url={https://zenodo.org/records/3451402}
+}
+```