Datasets:

agianbig
/

mixed_depth

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
      num_examples: 19279
  download_size: 421666211
  dataset_size: 466678289
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 · project page · arXiv).

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

Examples: 19,279 — 3-point: 6,736 · 4-point: 6,562 · 5-point: 5,981
Image resolution: 336 × 336 · Source imagery: ADE20K (train split)

Browse / load (parquet, default config)

All 19,279 images live (one per row, losslessly) in the parquet under data/, which also powers the viewer:

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, image_filename, question, answer, answer_letter, answer_type, num_points, point_labels, per-point point_{A..E}_x / _y (pixel coords) and point_{A..E}_depth.

Why no images/ folder? The Hugging Face Hub caps any directory at 10,000 files, and there are 19,279 images. They are therefore distributed inside the parquet rather than as a flat folder.

Finetuning (LLaVA / Qwen conversation format)

The conversation-format JSONs reference images by basename, so first extract the images to a local images/ folder from the parquet, then train:

import os
from datasets import load_dataset
ds = load_dataset("agianbig/mixed_depth", split="train")
os.makedirs("images", exist_ok=True)
for r in ds:
    r["image"].save(os.path.join("images", os.path.basename(r["image_filename"])))

hf download agianbig/mixed_depth --repo-type dataset --local-dir mixed_depth
# run the snippet above (cwd = mixed_depth) to create mixed_depth/images/, then:
#   --data_path    mixed_depth/mixed_depth_long.json   # CoT answers, or mixed_depth_short.json
#   --image_folder mixed_depth/images

mixed_depth_long.json — long chain-of-thought answers (coords + depth reasoning + final letter)
mixed_depth_short.json — short answers (final letter only, e.g. (C))

License

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