README.md

---
pretty_name: CommerceGen-Bench
language:
- zh
license: cc-by-nc-nd-4.0
task_categories:
- image-to-image
tags:
- e-commerce
- image-editing
- multi-image-editing
- visual-text-rendering
- benchmark
size_categories:
- n<1K
configs:
- config_name: single_image
  data_files:
  - split: test
    path: data/single_image_test.parquet
- config_name: multi_image
  data_files:
  - split: test
    path: data/multi_image_test.parquet
---

# CommerceGen-Bench

CommerceGen-Bench is an e-commerce product-poster generation and editing benchmark containing 400 test cases: 200 single-image editing cases and 200 multi-image editing cases. Each case includes fixed with-PE and without-PE instructions, Chinese target selling-point text, embedded input images, and a three-level product taxonomy.

The release contains test inputs only. It provides neither a training split nor reference output images. Input images are stored directly in the Parquet files, so no separate image directory or public image URL is required.

## Files and configurations

- `data/single_image_test.parquet`: 200 single-image editing cases.
- `data/multi_image_test.parquet`: 200 multi-image editing cases.
- `README.md`: dataset card, schema, protocol, limitations, and license.

The `single_image` and `multi_image` configurations each expose a `test` split containing 200 cases. Both prompt conditions share the same input images; there is no duplicated with-PE/without-PE image export.

There is intentionally no `default` configuration. Load the two tracks separately and concatenate them locally if a combined 400-case dataset is required. Keeping the physical Parquet files assigned to only one configuration avoids duplicated size accounting in the Hugging Face Dataset Viewer.

## Schema

| Field | Type | Meaning |
| --- | --- | --- |
| `sample_id` | string | Stable anonymized identifier drawn from `commercegen_0001` through `commercegen_0420`. IDs are non-contiguous because 20 cases were removed during curation. |
| `track` | string | `single_image_editing` or `multi_image_editing`. |
| `product_image` | Image | Primary product input. It is the only image input for single-image cases and the first image input for multi-image cases. |
| `logo_image` | Image or null | Auxiliary logo/reference input for multi-image cases; null for single-image cases. |
| `instruction_with_pe` | string | Frozen enhanced instruction used in the historical with-PE evaluation. |
| `instruction_without_pe` | string | Frozen strict no-PE instruction containing the five-requirement prefix, newline-separated selling points, and the strict suffix described below. |
| `target_text_list` | list of strings | Selling-point strings expected to appear in the generated poster. |
| `top_level_category` | string | One of 6 top-level product groups. |
| `category` | string | One of 15 product categories. |
| `subcategory` | string | Fine-grained product category. The current release contains 47 non-empty subcategories. |

Business identifiers, credentials, signed download URLs, private storage locations, and source filesystem information are not included in this public release. Embedded image fields contain image bytes only and do not expose source file locations.

## Dataset composition

### Tracks

| Track | Cases |
| --- | ---: |
| Single-image editing | 200 |
| Multi-image editing | 200 |
| **Total** | **400** |

### Top-level categories

| Top-level category | Cases | Share |
| --- | ---: | ---: |
| Beauty & Fashion | 157 | 39.25% |
| Home & Decor | 114 | 28.50% |
| 3C & Appliances | 50 | 12.50% |
| Sports & Baby | 25 | 6.25% |
| Food & Kitchen | 45 | 11.25% |
| Others | 9 | 2.25% |
| **Total** | **400** | **100.00%** |

### Product categories

| Category | Cases | Share |
| --- | ---: | ---: |
| Apparel | 57 | 14.25% |
| Footwear | 41 | 10.25% |
| Bags & Accessories | 28 | 7.00% |
| Beauty & Care | 31 | 7.75% |
| Home Textile | 22 | 5.50% |
| Lighting | 20 | 5.00% |
| Hardware | 40 | 10.00% |
| Furniture | 32 | 8.00% |
| Digital 3C | 27 | 6.75% |
| Home Appliances | 23 | 5.75% |
| Outdoor | 18 | 4.50% |
| Baby & Toys | 7 | 1.75% |
| Kitchenware | 24 | 6.00% |
| Food & Beverage | 21 | 5.25% |
| Pets & Miscellaneous | 9 | 2.25% |
| **Total** | **400** | **100.00%** |

The taxonomy was assigned according to the actual primary product shown in each case. Category counts are not artificially equalized.

## Image and task protocol

Pass `product_image` first. For a multi-image case, pass `logo_image` second as a separate image; do not concatenate the two inputs. Select exactly one instruction column for a given experiment and record the prompt condition together with all model settings.

The release does not prescribe a generation resolution. Model-specific input resizing, output size, sampling steps, guidance scale, random seed, and other generation parameters should be recorded separately.

Generated images should preserve the product identity and, for multi-image cases, accurately reproduce the auxiliary logo/reference condition. All requested selling-point text should be rendered correctly and completely according to the selected instruction.

## PE and no-PE protocol

`instruction_with_pe` contains the frozen enhanced instruction generated by Qwen3.8-Max for the experiments reported in the paper. These instructions are released as fixed benchmark inputs and are not regenerated at evaluation time.

`instruction_without_pe` uses the historical strict C variant: the fixed five-requirement poster prefix, the unnumbered selling points joined with newlines, followed by exactly:

```text
请严格按照我给的卖点内容进行生产,不允许添加新的卖点
```

The ASCII comma above is preserved exactly as used in the historical evaluation prompt.

The historical B variant used by some open-source runs omits the final suffix. B and C are not identical. To reproduce a documented B run, remove only the final newline and the exact suffix above; do not otherwise rewrite the instruction.

Prompt line breaks are preserved. Comma-compacted prompt variants used in presentation slides are not substituted into the released instruction fields.

## Evaluation

The paper evaluates generated images using Gemini 3 Flash with five dimensions:

- Product Fidelity (PF)
- Instruction Following (IF)
- Product Integration (PI)
- Naturalness Detection (ND)
- Artifacts Detection (AD)

Overall is the arithmetic mean of the five dimension scores:

```text
Overall = (PF + IF + PI + ND + AD) / 5
```

Single-image and multi-image tracks must be reported separately. With-PE and without-PE conditions must also be reported separately.

For reproducible comparisons, record the exact generation model version, inference configuration, random seed, judge model ID, judge decoding configuration, evaluation prompt version, number of repeated judgments, and score-aggregation procedure.

Because commercial poster generation admits multiple valid layouts and visual treatments, this benchmark does not use a single pixel-level reference as the primary evaluation target.

## Important reproducibility notes

The historical PE instructions are preserved without silently repairing them:

- 29 cases do not contain at least one exact target selling-point string after removing whitespace differences (31 missing strings in total). This substring check does not prove that the corresponding semantic meaning is absent.
- 67 cases contain an image marker beyond the number of actual inputs, including references to `[image3]` although cases have at most two input images.
- The historical PE instruction for `commercegen_0301` includes a merchant storefront address. This case must be reviewed before public distribution.

Any corrected prompt set should be released as a separately versioned configuration rather than silently replacing the prompts used in historical evaluations.

## Load from Hugging Face

Install a recent compatible environment:

```bash
python -m pip install -U datasets pyarrow Pillow
```

Load the two tracks separately:

```python
from datasets import load_dataset, concatenate_datasets

single = load_dataset(
    "TaobaoTmall-AlgorithmProducts/CommerceGen-Bench",
    "single_image",
    split="test",
)

multi = load_dataset(
    "TaobaoTmall-AlgorithmProducts/CommerceGen-Bench",
    "multi_image",
    split="test",
)

assert len(single) == 200
assert len(multi) == 200

dataset = concatenate_datasets([single, multi])
assert len(dataset) == 400

sample = multi[0]
input_images = [sample["product_image"], sample["logo_image"]]
prompt = sample["instruction_with_pe"]
```

Private repositories require authentication by an account with access.

## Load local Parquet files

```python
from datasets import load_dataset, concatenate_datasets

single = load_dataset(
    "parquet",
    data_files={"test": "data/single_image_test.parquet"},
    split="test",
)

multi = load_dataset(
    "parquet",
    data_files={"test": "data/multi_image_test.parquet"},
    split="test",
)

dataset = concatenate_datasets([single, multi])
assert len(dataset) == 400
```

## License

CommerceGen-Bench is released under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License.

- The dataset may be copied and redistributed for non-commercial purposes with appropriate attribution.
- Commercial use is prohibited.
- Modified or adapted versions of the dataset may not be publicly distributed.

License terms: https://creativecommons.org/licenses/by-nc-nd/4.0/

Brand names and trademarks remain the property of their respective owners. Use of this dataset must also comply with applicable privacy, publicity, trademark, and other third-party rights.

## Citation

```bibtex
@article{xu2026commercegenstudio,
  title={CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image Generation},
  author={Xu, Xiaoli and Wu, Zhengtao and Xu, Zhengze and others},
  year={2026}
}
```

Please replace this provisional citation with the full author list and arXiv or conference metadata when available.