README.md

---
license: other
task_categories:
- visual-question-answering
language:
- en
pretty_name: AutoV Attention-Map Training Data
---

# AutoV Attention-Map Training Data

This dataset repository contains the training metadata and derived
attention-map images used by AutoV. It does not contain the original source
images.

## Contents

### Training data

- `metadata/llava_v1_5_mix100k_reward.json`: 100,000 reward-training records.
- `metadata/llava_v1_5_mix100k_filter.json`: the corresponding filtered records.
- `metadata/llava_v1_5_mix1k_*.json`: the 1,000-record development subset.
- `archives/attnmap100K/`: 20 tar archives containing 400,000 attention maps.
- `archives/attnmap1K/`: 4 tar archives containing 4,000 attention maps.
- `SHA256SUMS`: integrity checks for metadata and archives.

### Inference data

- `inference-data/mmvet/`: 800 MMVet attention-map images (200 image names
  across CLIP ViT-L/14@336 layers 15, 20, 22, and 23). The directory layout
  matches `LLaVA/playground/data/api_eval/mmvet`.

The 100K metadata references 25,000 COCO, 20,000 Visual Genome, 20,000 GQA,
20,000 OCR-VQA, and 15,000 TextVQA source-image paths. Each record has four
derived attention maps from CLIP ViT-L/14@336 layers 15, 20, 22, and 23.

## Restore the original AutoV data layout

Run from the downloaded repository root:

```bash
mkdir -p data/attnmap100K data/attnmap1K
for archive in archives/attnmap100K/*.tar; do
  tar -xf "$archive" -C data/attnmap100K
done
for archive in archives/attnmap1K/*.tar; do
  tar -xf "$archive" -C data/attnmap1K
done
cp metadata/* data/
sha256sum -c SHA256SUMS
```

## Licensing note

The metadata refers to COCO, Visual Genome, GQA, OCR-VQA, TextVQA, and LLaVA
data. Their original licenses and terms continue to apply. Redistribution of
the derived attention-map images was reviewed against the applicable source
terms before publication.