Judge Dataset Documentation
Overview
The Judge dataset evaluates how well vision-language models (VLMs) can act as judges of computer vision model outputs. Each prompt presents a VLM with one or more encoded predictions and asks it to assess quality — via pairwise comparison, ranking, or absolute scoring. The dataset covers 15 vision tasks, 53 encoding variants, and three question types.
Dataset location: Icey444/Judge_questions_v2 on Hugging Face
v2.1 split: 5,549 items (2,855 pairwise · 2,647 scoring · 47 ranking)
1. Task Coverage
| Task | Category | What is judged |
|---|---|---|
object_detection |
Perception | Bounding boxes (label + coordinates) for specified classes |
instance_segmentation |
Perception | Per-instance pixel masks for specified classes |
semantic_segmentation |
Perception | Per-class pixel masks across all categories |
referring_segmentation |
Perception | Mask of the region referred to by a natural-language expression |
keypoint |
Perception | 17-keypoint COCO pose skeleton per person |
depth_estimation |
Perception | Dense monocular depth map |
lowlevel-deblur |
Restoration | Image deblurring result |
lowlevel-derain |
Restoration | Image deraining result |
lowlevel-desnow |
Restoration | Image desnowing result |
lowlevel-super-resolution |
Restoration | Image super-resolution result |
generation_controllable |
Generation | Controllable image generation (control condition + prompt) |
generation_editing |
Generation | Instruction-based image editing |
generation_inpainting_high_level |
Generation | High-level inpainting (semantic fill) |
generation_inpainting_low_level |
Generation | Low-level inpainting (seamless texture fill) |
generation_t2i |
Generation | Text-to-image generation |
2. Encoding Variants
Encoding variants determine how a model's prediction is presented to the judge. Each variant transforms raw annotation output (bounding boxes, masks, keypoints, etc.) into a form the VLM can read. There are two broad families: pixel (rendered image) and text (structured string). Combo encodings pair one text and one pixel form per option.
2.1 Object Detection (6 variants)
| Stem | Type | Description |
|---|---|---|
pixel_s0_m0 |
pixel | Colored bounding boxes, no label text, overlaid on original image |
pixel_s1_m0 |
pixel | Colored boxes + label text, overlaid on original image |
pixel_s1_m1 |
pixel | Colored boxes + label text, rendered on separate black canvas |
0305 |
combo | text_xyxy coordinates + pixel_s1_m0 box image, per option |
text_xyxy |
text | {"label":…,"bbox":[x1,y1,x2,y2]} per detection |
text_xywh |
text | {"label":…,"bbox":[x,y,w,h]} per detection |
2.2 Instance Segmentation (12 variants)
Sub-sampled pixel (downsampled grid, each cell = most-covered instance index):
| Stem | Embed | Description |
|---|---|---|
pixel_ss0_m0 |
overlay | Grid overlaid on original image |
pixel_ss0_m1 |
separate | Grid on black canvas |
pixel_ss1_m0_o0_l0_c0_b0 |
overlay | … (sub-sampled text) |
Original-resolution pixel (full-resolution mask rendering):
| Stem | Opacity | Label overlay | Color scheme | Bbox style |
|---|---|---|---|---|
pixel_ss1_m0_o0_l1_c0_b1 |
0.5 | text labels | color-by-class | dashed bbox |
pixel_ss1_m0_o0_l1_c1_b1 |
0.5 | text labels | color-by-instance | dashed bbox |
pixel_ss1_m0_o1_l1_c0_b1 |
1.0 | text labels | color-by-class | dashed bbox |
pixel_ss1_m1_o0_l1_c0_b1 |
0.5 | text labels | color-by-class | dashed bbox, separate canvas |
pixel_ss1_m0_o0_l1_c0_b0 |
0.5 | text labels | color-by-class | no bbox |
pixel_ss1_m0_o0_l0_c0_b1 |
0.5 | no labels | color-by-class | dashed bbox |
Text-only:
| Stem | Format |
|---|---|
text_polygon |
{"instance_id":…,"label":…,"polygon":[[x,y],…]} per instance |
text_rle |
{"instance_id":…,"label":…,"rle":{…}} COCO RLE per instance |
text_matrix |
2D integer grid (rows × cols), each cell = instance index |
Combo: 1742 — polygon text + color-by-instance image, per option.
2.3 Semantic Segmentation (8 variants)
Sub-sampled pixel (3): overlay, separate canvas, text sub-sample.
Original-resolution pixel (4, all opacity 0.5):
| Stem | Label overlay | Color scheme | Canvas |
|---|---|---|---|
pixel_ss1_m0_o0_l1_c0 |
text labels | standard palette | overlay |
pixel_ss1_m0_o0_l1_c1 |
text labels | random colors | overlay |
pixel_ss1_m0_o1_l1_c0 |
text labels | standard palette | overlay, full opacity |
pixel_ss1_m1_o0_l1_c0 |
text labels | standard palette | separate canvas |
pixel_ss1_m0_o0_l0_c0 |
no labels | standard palette | overlay |
Text-only:
| Stem | Format |
|---|---|
text_polygon |
{"label":…,"polygon":[[x,y],…]} per segment |
text_matrix |
2D integer grid, each cell = class index |
Combo: 4649 — sub-sample text + original-res overlay image, per option.
2.4 Referring Segmentation (11 variants)
Sub-sampled pixel (3): overlay, separate, text sub-sample.
Original-resolution pixel (5):
| Stem | Mask style | Opacity | Canvas |
|---|---|---|---|
pixel_ss1_m0_o0_m0 |
filled region | 0.5 | overlay |
pixel_ss1_m0_o0_m1 |
contour only | 0.5 | overlay |
pixel_ss1_m0_o0_m2 |
fill + contour | 0.5 | overlay |
pixel_ss1_m0_o1_m0 |
filled region | 1.0 | overlay |
pixel_ss1_m1_o0_m0 |
filled region | 0.5 | separate canvas |
Text-only:
| Stem | Format |
|---|---|
text_polygon |
{"label":"<expression>","polygon":[[x,y],…]} |
text_matrix |
2D grid; legend maps index → referring expression |
Combo: 7080 — polygon text + fill+contour image, per option.
2.5 Keypoint Detection (8 variants)
Pixel:
| Stem | Style | Color scheme | Canvas |
|---|---|---|---|
pixel_s0_c1_m0 |
points only | color-by-instance | overlay |
pixel_s1_c0_m0 |
skeleton | single color (green) | overlay |
pixel_s1_c1_m0 |
skeleton | color-by-instance | overlay |
pixel_s1_c2_m0 |
skeleton | color-by-body-part | overlay |
pixel_s1_c1_m1 |
skeleton | color-by-instance | separate canvas |
Text-only:
| Stem | Format |
|---|---|
text_flat_list |
34 numbers [x0..x16, y0..y16] per person (COCO order) |
text_part_keyed_json |
{"person_id":…,"keypoints":[{"name":…,"x":…,"y":…},…]} |
text_coco_style |
51 numbers [x,y,v]×17 per person |
All text formats include the note: x=0.0, y=0.0 means the keypoint was not detected or is not visible.
2.6 Depth Estimation (3 variants)
Each variant is a colormap applied to the predicted depth map:
| Stem | Colormap | Semantics |
|---|---|---|
plasma |
Magma/plasma | Bright yellow = closest, dark purple = farthest |
turbo |
Turbo (rainbow) | Red = closest, blue = farthest |
gray |
Grayscale | Bright = closest, dark = farthest |
2.7 Low-level Restoration (4 tasks × 1 variant each)
Each task has a single pixel encoding: the restored output image shown alongside the degraded input.
| Task | Input context |
|---|---|
lowlevel-deblur |
Blurry source image |
lowlevel-derain |
Rainy source image |
lowlevel-desnow |
Snowy source image |
lowlevel-super-resolution |
Low-resolution source image |
2.8 Image Generation (5 tasks × 1 variant each)
Each task shows the generated output image(s) alongside the source context.
| Task | Source context shown |
|---|---|
generation_controllable |
Source image (control signal + reference) |
generation_editing |
Original image before editing |
generation_inpainting_high_level |
Original image with masked region |
generation_inpainting_low_level |
Original image with masked region |
generation_t2i |
No source image (text prompt only) |
3. Question Types
3.1 Pairwise Comparison
The judge sees two options (A and B) and selects the better prediction.
Structure:
[<image>] ← original/reference image (if available)
You are a judge to decide the quality of answers to a <task> task [based on my given image].
[Task-specific context: class(es) of interest / referring prompt / etc.]
Format of predictions: <encoding description>
Options:
A. [<image>] [text or legend]
B. [<image>] [text or legend]
<Final question>. Please answer with A or B.
Pair sampling: Within each (image_id, class-of-interest, error_type, prompt) group, pairs are drawn so that no two annotations with equal final_score are paired. Up to 10 pairs per group per task (encoding_analysis) or 50 pairs (judge_analysis).
Answer: The letter corresponding to the annotation with the higher final_score.
Final question phrasing is sampled from five paraphrases to reduce positional bias:
- Which prediction is better?
- Which option is a better execution of the vision task?
- Which option would you prefer as answer to the vision task?
- Which of the two is the better result?
- Which option better fulfills the task?
3.2 Ranking
The judge sees N options (A through E, or fewer) and ranks them best-to-worst.
Structure:
[original image context]
You are a judge to decide the quality of answers to a <task> task.
[Task-specific context]
Format of predictions: <encoding description>
Options:
A. [<image> or text]
B. [<image> or text]
...
Rank the predictions from best to worst. Respond with the ranking as a single string
of letters only (best first, worst last). For example, BCAED.
Groups of 3–5 annotations sharing the same (image_id, class-of-interest, error_type) are ranked together. Used in judge_analysis only.
Answer: Letters ordered by descending final_score.
3.3 Scoring
The judge sees a single prediction and assigns a score from 0 to 10.
Structure:
[original image]
You are a judge to decide the quality of answers to a <task> task [based on my given image].
[Task-specific context]
Format of prediction: <encoding description>
[First image: original. Second image: encoded prediction.] ← pixel encodings
[Prediction (text): <content>] ← text encodings
Score the quality of the prediction from 0 to 10.
0 = random guessing / worst, 10 = best possible.
Please answer with a single score from 0 to 10 only.
Answer: The annotation's final_score (normalized to 0–10).
Used in judge_analysis only. 20 groups × 5 annotations per group × stems per task.
4. Prompt Construction Standards
4.1 Role Framing
Every prompt begins with a judge role sentence tailored to the task:
| Task group | Intro pattern |
|---|---|
| Object detection | "You are a judge to decide the quality of answers to an object detection task. The class(es) of interest is {coi}." |
| Instance / semantic segmentation | Same pattern with respective task name and COI |
| Referring segmentation | "… The prompt is '{expression}'." (or "The prompt is the referring expression shown in the options below." if not available at prompt-level) |
| Keypoint | "… The task is pose estimation." |
| Depth estimation | "… The task is depth prediction." |
| Low-level restoration | Task-specific sentence describing the restoration goal |
| Generation | Task-specific sentence describing the generation goal + text prompt when available |
"based on my given image" is appended when the original image is included as a <image> placeholder.
4.2 Format Description
After the role sentence, the prompt includes a Format of predictions block describing the encoding so the judge knows what it is looking at:
- Pixel encodings: describe the visual style (overlay/canvas, color scheme, opacity, label style).
- Text encodings: describe the schema (e.g., JSON structure, coordinate conventions, grid dimensions and legend).
- Combo encodings: each option shows its own format description inline, followed by the encoded image.
- Generation/low-level: no format description (the prediction is a natural image); the instruction covers the task criterion instead.
4.3 Color Legends
For encodings where colors carry semantic meaning, a legend is included per option (not once globally), because different predictions may contain different classes or instances:
- Object detection pixel: legend lists each detected class and its assigned color.
- Instance segmentation (color-by-class): legend lists each class and color.
- Instance segmentation (color-by-instance): no per-instance legend (color only distinguishes people; skeleton structure is self-evident).
- Semantic segmentation: legend lists each class and color.
- Keypoint (color-by-part): legend lists each of the 17 COCO keypoint names and its color.
- Keypoint (color-by-instance): one sentence describing that all keypoints and links of the same person share a color; no per-person list.
- Keypoint (same color): "All keypoints and links use a single color (green). No color legend."
- Depth colormaps: the colormap semantics (which end is near/far) are described in the format block.
4.4 Image Placeholder Ordering
<image> placeholders in the prompt correspond to media entries in the same order:
- Original/reference image (first, when present) — always
original_{image_id}.png. - Option A image (prediction rendered for annotation A).
- Option B image (prediction rendered for annotation B).
Text-only encodings include only the original image (1 image total). Generation/low-level pixel encodings that have no source image in the image index include 2 images (A and B only). Generation tasks with a retrievable source image include 3 images.
4.5 Subset Labels
Each item carries a subset field indicating which run produced it:
| Subset | Stems used | Samples | Pairs | Scoring/Ranking |
|---|---|---|---|---|
encoding_analysis |
All is_base_ablation=1 (51 regular + gen/lowlevel) |
10 per task | 10 per task | No |
judge_analysis |
is_final=1 (53) |
20 per task | 50 per task | Yes (20 groups × 5 annotations) |
Both runs use seed=42. The v2.1 JSON is the deduplicated union (keyed on task + encoding + question_type + annotation IDs).
5. Data Sources
| Task group | Annotation source | Image source |
|---|---|---|
| Perception tasks | data_json_v2/*_annotations.json |
images_v2/*.json → local files or HF URLs |
| Low-level restoration | data_json_v2/lowlevel_annotations.json |
HF: Icey444/VisualJudge_images (prediction URLs) + images_v2/lowlevel.json (source URLs) |
| Generation | data_json_v2/generation_annotations.json |
HF: Icey444/VisualJudge_images (prediction URLs) + images_v2/generation_images.json (source URLs) |
Predictions are encoded locally by task-specific encoder scripts (src/encoders/encode_*.py) and stored under output/encoded_v2/. Original images are cached as original_{image_id}.png in the same directory.