---
license: mit
task_categories:
- visual-question-answering
- depth-estimation
language:
- en
tags:
- depth
- spatial-reasoning
- computer-vision
- multimodal
- vlm
- ablate-to-validate
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: id
    dtype: string
  - name: image_filename
    dtype: string
  - name: original_image
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: answer_letter
    dtype: string
  - name: answer_type
    dtype: string
  - name: num_points
    dtype: int64
  - name: point_labels
    sequence: string
  - name: depth_token
    dtype: string
  - name: point_A_x
    dtype: int64
  - name: point_A_y
    dtype: int64
  - name: point_B_x
    dtype: int64
  - name: point_B_y
    dtype: int64
  - name: point_C_x
    dtype: int64
  - name: point_C_y
    dtype: int64
  - name: point_D_x
    dtype: int64
  - name: point_D_y
    dtype: int64
  - name: point_E_x
    dtype: int64
  - name: point_E_y
    dtype: int64
  - name: point_A_depth
    dtype: float64
  - name: point_B_depth
    dtype: float64
  - name: point_C_depth
    dtype: float64
  - name: point_D_depth
    dtype: float64
  - name: point_E_depth
    dtype: float64
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 466678289.0
    num_examples: 19279
  download_size: 421666211
  dataset_size: 466678289.0
pretty_name: 'Mixed-Depth: Relative-Depth Point QA (ADE20K)'
---

# Mixed-Depth: Relative-Depth Point QA (ADE20K)

Training data for **Ablate-to-Validate: Are Vision-Language Models Really Using Visual Reasoning Tokens?**
([code](https://github.com/tjazhang/ablate_to_validate) &middot; [project page](https://tjazhang.github.io/ablate_to_validate/) &middot; [arXiv](https://arxiv.org/abs/2605.21642)).

Each example shows an ADE20K image with **3, 4, or 5 labeled points** circled and asks **which point is
closest to the camera**. It is used to train the LLaVA and Qwen2.5-VL relative-depth models in the paper.

- **Examples:** 19,279 &mdash; 3-point: 6,736 &middot; 4-point: 6,562 &middot; 5-point: 5,981
- **Image resolution:** 336 &times; 336
- **Source imagery:** ADE20K (train split)

## Browse / load (parquet, default config)

The default config powers the dataset viewer and loads directly:

```python
from datasets import load_dataset
ds = load_dataset("agianbig/mixed_depth", split="train")
ds[0]["image"]          # PIL.Image (336x336)
ds[0]["answer_letter"]  # e.g. "C"
```

Columns: `id`, `image`, `question`, `answer`, `answer_letter`, `answer_type`, `num_points`,
`point_labels`, per-point `point_{A..E}_x` / `point_{A..E}_y` (pixel coords) and
`point_{A..E}_depth` (relative depth).

## Finetuning files (LLaVA / Qwen conversation format)

For training, use the conversation-format JSON plus the image folder. Image fields are **basenames**
relative to `images/`:

```bash
hf download agianbig/mixed_depth --repo-type dataset --local-dir mixed_depth
# then, e.g.:
#   --data_path   mixed_depth/mixed_depth_long.json   # or mixed_depth_short.json
#   --image_folder mixed_depth/images
```

- `mixed_depth_long.json` &mdash; long chain-of-thought answers (point coordinates + depth reasoning + final letter)
- `mixed_depth_short.json` &mdash; short answers (final letter only, e.g. `(C)`)
- `images/` &mdash; 19,279 JPGs, referenced by basename in the JSON

## License

Released under MIT. Imagery is derived from ADE20K; please also observe the ADE20K terms.