[Sample] V-CAPE: Visual Consistency Assessment for Product Image Editing

V-CAPE Data Sample

A small stratified sample from the V-CAPE benchmark, intended for anonymous reviewer quality inspection.

The sample contains 3 examples per class (label × rejection reason) from each of the two dataset splits:

Split
Description
Classes
Samples

vcape-r 
Real e-commerce editing pairs
accepted, Geometry Artifacts, Wrong Orientation, Irrelevant Objects, Texture/Lighting/Color Issues, Background Issues, Generated-Main Image Mismatch
21

vcape-s
Synthetic pairs rendered from 3D assets
accepted, wrong_orientation, wrong_product_different_pt, wrong_product_same_pt
12

Files

File
Description

data-00000-of-00001.arrow
HuggingFace dataset in Arrow format containing all 33 samples

dataset_info.json
HuggingFace dataset metadata (features, num rows)

state.json
HuggingFace dataset state file

croissant-sample.json
Croissant metadata describing the dataset schema

image_downloader.py
Standalone script to download vcape-r source images by physical_id

visualize_datasample.py
Generates a self-contained HTML report for visual inspection

report.html
Pre-generated HTML visualization with the data sample (open in any browser)

Dataset Schema

Each row contains the following columns:

Column
Type
Description

split
string
Origin split: `vcape-r` or `vcape-s`

physical_id
string
Source image identifier for vcape-r rows (empty for vcape-s)

xsource_image
Image
Embedded source image for vcape-s rows (null for vcape-r)

xtarget_image
Image
Generated/rendered target image

object_description
string
Natural-language description of the product

product_type
string
Product category (e.g., "sofa")

pose
string 
Target orientation / pose label

input_prompt
string 
Editing prompt for the transformation

label
string
Human quality label: `accepted` / `rejected` (vcape-r) or `accept` / `reject` (vcape-s)

rejection_reason
string
Structured rejection reason from a predefined taxonomy

Quick Start

1. Install dependencies

```bash

pip install datasets Pillow requests

```

2. Load the dataset

```python

from datasets import load_from_disk

ds = load_from_disk(".") # run from this directory

print(ds)

print(ds[0])

```

3. Download vcape-r source images

```bash

# Download all vcape-r source images into source_images/

python image_downloader.py

# Or download specific images

python image_downloader.py --pids 91FmmLIE3GL 71tLzgZoBQL

```

4. Visualize the sample

Either open the pre-generated `report.html` in a browser, or regenerate it:

```bash

python visualize_datasample.py

```

This produces a self-contained HTML file with image thumbnails, metadata, and filter controls for split, label, and rejection reason.