ECom-RF-IMMR and Adapted Public Benchmarks

This repository contains four image-to-multimodal item retrieval datasets:

• 1_ECom_RF_IMMR_Normal
• 2_ECom_RF_IMMR_Mosaic
• 3_eSSPR
• 4_LookBench

The first two datasets are our constructed ECom-RF-IMMR evaluation datasets. The last two datasets are adapted from public e-commerce retrieval benchmarks.

These datasets are designed for image-to-multimodal item retrieval (IMMR), where the query is an image region and each candidate item is represented by an item image and structured item text. This setting reflects practical e-commerce visual search, where a localized product query needs to retrieve item candidates that may appear in full images with background context, multiple objects, or visual distractors.

Dataset Overview

Dataset	Source	Query Image	Item Image	Box Annotation	Text Annotation	Main Usage
1_ECom_RF_IMMR_Normal	Ours	Original product image	Original product image	Query box and item box	Item title	Clean item retrieval
2_ECom_RF_IMMR_Mosaic	Ours	Original product image	Mosaic image with distractors	Query box and item box	Item title	Cluttered item retrieval
3_eSSPR	Public dataset adapted to IMMR	Product image	Product image	Generated by detector and VLM filtering	Translated item title	Public benchmark evaluation
4_LookBench	Public dataset adapted to IMMR	Street-look or fashion image	Product image	Original box annotation	Generated and translated item title	Noisy and one-to-many retrieval

Directory Structure

.
├── 1_ECom_RF_IMMR_Normal/
│   ├── dataset.jsonl
│   └── img/
│       ├── 0000/
│       │   ├── 00000000_query.jpg
│       │   ├── 00000000_item.jpg
│       │   └── ...
│       └── ...
│
├── 2_ECom_RF_IMMR_Mosaic/
│   ├── dataset.jsonl
│   └── img/
│       ├── 0000/
│       │   ├── 00000000_query.jpg
│       │   ├── 00000000_item.jpg
│       │   └── ...
│       └── ...
│
├── 3_eSSPR/
│   ├── esspr.jsonl
│   └── img/
│       ├── query/
│       └── item/
│
└── 4_LookBench/
    ├── aigen_streetlook.jsonl
    ├── aigen_studio.jsonl
    ├── real_streetlook.jsonl
    ├── real_studio_flat.jsonl
    ├── look_bench_noise.jsonl
    └── img/
        ├── query/
        ├── item/
        └── noise/

All image paths in the annotation files are relative paths from the parent directory that contains the four dataset folders.

Task Definition

Given a query image and a query box, the task is to retrieve the matched item from a candidate pool. Each item candidate is represented by an item image and an item title. The box annotations indicate the target region in the query image and item image.

The box format is:

[x1, y1, x2, y2]

where (x1, y1) and (x2, y2) are the top-left and bottom-right coordinates in pixel space.

1. ECom-RF-IMMR-Normal

1_ECom_RF_IMMR_Normal is our clean evaluation dataset. Both query and item images are original product images. Each sample contains a query-side target box, an item-side target box, and an item title.

Only the following five fields are retained:

Field	Type	Description
query_path	string	Relative path to the query image
query_box	list[float]	Target box in the query image
item_path	string	Relative path to the item image
item_box	list[float]	Target box in the item image
item_title	string	Item title text

Images are stored under 1_ECom_RF_IMMR_Normal/img/. To avoid having too many images in a single folder, images are split into subfolders. Each query and item image is renamed according to the reading order, padded to eight digits, with _query or _item as the suffix.

Example:

{
  "query_path": "1_ECom_RF_IMMR_Normal/img/0000/00000000_query.jpg",
  "query_box": [88.277435, 311.30823, 305.92258, 618.63495],
  "item_path": "1_ECom_RF_IMMR_Normal/img/0000/00000000_item.jpg",
  "item_box": [74.85748, 191.0392, 426.9335, 479.08618],
  "item_title": "春款0-1岁宝宝鞋 爱心绣花蝴蝶结公主鞋 软底防滑防掉舒适婴儿鞋"
}

2. ECom-RF-IMMR-Mosaic

2_ECom_RF_IMMR_Mosaic is our cluttered evaluation dataset. The query image is the original product image, while the item image is a synthesized Mosaic image containing the target item together with additional distractor items.

This dataset evaluates retrieval robustness under complex multi-item layouts. The query image, query box, and item title are kept consistent with the corresponding Normal sample, while the item image is reconstructed as a cluttered scene.

Only the following five fields are retained:

Field	Type	Description
query_path	string	Relative path to the query image
query_box	list[float]	Target box in the query image
item_path	string	Relative path to the Mosaic item image
item_box	list[float]	Target box of the item in the Mosaic image
item_title	string	Item title text

Example:

{
  "query_path": "2_ECom_RF_IMMR_Mosaic/img/0000/00000000_query.jpg",
  "query_box": [88.277435, 311.30823, 305.92258, 618.63495],
  "item_path": "2_ECom_RF_IMMR_Mosaic/img/0000/00000000_item.jpg",
  "item_box": [51.0, 60.0, 172.0, 159.0],
  "item_title": "春款0-1岁宝宝鞋 爱心绣花蝴蝶结公主鞋 软底防滑防掉舒适婴儿鞋"
}

3. Adapted eSSPR

3_eSSPR is adapted from the public eSSPR dataset for the IMMR setting. Duplicate or problematic samples are filtered. Since the original dataset does not provide box annotations, we first use an object detection model to generate candidate boxes, and then use Qwen-VL-Plus to select the box corresponding to the main product. Item titles are translated using DeepSeek.

Annotation file:

3_eSSPR/esspr.jsonl

Fields:

Field	Type	Description
query_path	string	Relative path to the query image
query_box	list[int]	Generated target box in the query image
query_title	string	Query-side title
item_id	string	Item identifier
item_path	string	Relative path to the item image
item_box	list[int]	Generated target box in the item image
item_catename	string	Item category name
item_title	string	Translated item title

Example:

{
  "query_path": "3_eSSPR/img/query/H1701c702fb7847a99b53f5438b3042d8e.jpg",
  "query_box": [392, 93, 749, 703],
  "query_title": "NS040 亮片羽毛两件套裙装 女装裙 夏季性感夜店连衣裙",
  "item_id": "85777126353880",
  "item_path": "3_eSSPR/img/item/Hfe78da61ff4f4f43816161eba56da0b5L.jpg",
  "item_box": [186, 159, 501, 746],
  "item_catename": "服装#休闲连衣裙",
  "item_title": "女士蕾丝羽毛短袖休闲显瘦连衣裙时尚"
}

4. Adapted LookBench

4_LookBench is adapted from the public LookBench dataset. The original dataset provides box annotations. For each item, we summarize the category, main attribute, and other attributes into an item title using DeepSeek, and then translate the generated title using Qwen3-VL-32B.

LookBench contains four main evaluation subsets:

File	Description
aigen_streetlook.jsonl	AI-generated street-look subset
aigen_studio.jsonl	AI-generated studio subset
real_streetlook.jsonl	Real street-look subset
real_studio_flat.jsonl	Real studio-flat subset

Each sample may contain multiple matched items under the item field, making this dataset suitable for one-to-many retrieval evaluation.

Fields:

Field	Type	Description
query_path	string	Relative path to the query image
query_box	list[int]	Target box in the query image
query_title	string	Generated query-side title
item	list[dict]	List of matched item candidates

Each element in item contains:

Field	Type	Description
item_id	int	Item identifier within the sample
item_path	string	Relative path to the item image
item_box	list[int]	Target box in the item image
item_catename	string	Item category name
item_title	string	Generated and translated item title

Example:

{
  "query_path": "4_LookBench/img/query/raw_image_1bdb8f5d15ae4600a9bee93965270474.jpg",
  "query_box": [717, 468, 928, 674],
  "query_title": "鳄鱼皮 皮革,印花,贴花 包袋",
  "item": [
    {
      "item_id": 0,
      "item_path": "4_LookBench/img/item/raw_image_eb689d3192b94dfb80ddee5b7ee7c142.jpg",
      "item_box": [0, 14, 225, 189],
      "item_catename": "手提包",
      "item_title": "新款时尚鳄鱼纹皮革手提包 通勤百搭大容量轻奢女包"
    }
  ]
}

Noise Items

LookBench also includes noise candidates. These samples do not contain query fields or box annotations, and are used as distractor item candidates.

Annotation file:

4_LookBench/look_bench_noise.jsonl

Fields:

Field	Type	Description
item_path	string	Relative path to the noise item image
item_title	string	Generated item title

Example:

{
  "item_path": "4_LookBench/img/noise/image_267c9f0b100d4f2fac9170805439007e.jpg",
  "item_title": "新款复古印花挂脖连衣裙女时尚百搭显瘦A字裙"
}

Loading Example

import json
from pathlib import Path


def load_jsonl(path):
    samples = []
    with open(path, "r", encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if line:
                samples.append(json.loads(line))
    return samples


root = Path("/path/to/multimodel_datasets")

normal_samples = load_jsonl(root / "1_ECom_RF_IMMR_Normal" / "dataset.jsonl")
mosaic_samples = load_jsonl(root / "2_ECom_RF_IMMR_Mosaic" / "dataset.jsonl")
esspr_samples = load_jsonl(root / "3_eSSPR" / "esspr.jsonl")

lookbench_files = [
    "aigen_streetlook.jsonl",
    "aigen_studio.jsonl",
    "real_streetlook.jsonl",
    "real_studio_flat.jsonl",
]

lookbench_samples = []
for filename in lookbench_files:
    lookbench_samples.extend(load_jsonl(root / "4_LookBench" / filename))

lookbench_noise = load_jsonl(root / "4_LookBench" / "look_bench_noise.jsonl")

print(normal_samples[0])
print(mosaic_samples[0])
print(esspr_samples[0])
print(lookbench_samples[0])
print(lookbench_noise[0])

Evaluation Protocol

For 1_ECom_RF_IMMR_Normal, 2_ECom_RF_IMMR_Mosaic, and 3_eSSPR, each query is associated with a matched item. Standard retrieval metrics such as Recall@K, MRR@K, and NDCG@K can be used.

For 4_LookBench, each query may be associated with multiple matched items. In addition to Recall@K, MRR@K, and NDCG@K, HitRate@K can also be used for one-to-many retrieval evaluation.

Notes

• query_path and item_path are relative paths.
• query_box and item_box use [x1, y1, x2, y2] pixel coordinates.
• For the two ECom-RF-IMMR datasets, only five fields are kept: query_path, query_box, item_path, item_box, and item_title.
• For adapted public datasets, additional fields such as query_title, item_id, and item_catename are retained when available.
• Item-side boxes are provided for dataset annotation, supervision, and evaluation. Retrieval models may choose to encode only the full item image and item title during indexing and retrieval.
• Noise items in LookBench are used as distractor candidates and do not contain query-side annotations.

Download and Extraction

The image files are stored as tar shards under the img_shards/ directory of each dataset folder.
This is only for upload and storage convenience. After extraction, the directory structure will be restored to the same layout used by the annotation files.

After downloading this repository from Hugging Face, enter the repository root directory, which should contain the following dataset folders:

1_ECom_RF_IMMR_Normal/
2_ECom_RF_IMMR_Mosaic/
3_eSSPR/
4_LookBench/
README.md

Then run:

find . -path "*/img_shards/*.tar" -print0 | sort -z | xargs -0 -I{} tar -xf "{}"

Each tar shard already stores files with dataset-level relative paths, such as:

1_ECom_RF_IMMR_Normal/img/0000/00000000_query.jpg
2_ECom_RF_IMMR_Mosaic/img/0000/00000000_item.jpg
3_eSSPR/img/query/example.jpg
4_LookBench/img/item/example.jpg

Therefore, extracting all shards from the repository root will restore the expected image directories directly.

After extraction, the dataset structure should be:

.
├── 1_ECom_RF_IMMR_Normal/
│   ├── dataset.jsonl
│   ├── img/
│   └── img_shards/
├── 2_ECom_RF_IMMR_Mosaic/
│   ├── dataset.jsonl
│   ├── img/
│   └── img_shards/
├── 3_eSSPR/
│   ├── esspr.jsonl
│   ├── img/
│   └── img_shards/
└── 4_LookBench/
    ├── aigen_streetlook.jsonl
    ├── aigen_studio.jsonl
    ├── real_streetlook.jsonl
    ├── real_studio_flat.jsonl
    ├── look_bench_noise.jsonl
    ├── img/
    └── img_shards/

The paths in all annotation files are relative to the repository root.
For example, a path such as:

1_ECom_RF_IMMR_Normal/img/0000/00000000_query.jpg

can be opened as:

from pathlib import Path

root = Path("/path/to/downloaded/repository")
image_path = root / "1_ECom_RF_IMMR_Normal/img/0000/00000000_query.jpg"

The img_shards/ directories can be kept for reproducibility or removed after successful extraction.

Citation

If you use this dataset or find it helpful for your research, please cite:

@article{sun2026tiger,
  title={TIGER-FG: Text-Guided Implicit Fine-Grained Grounding for E-commerce Retrieval},
  author={Sun, Xinyu and Dai, Huangyu and Mao, Lingtao and Zheng, Zexin and Liang, Zihan and Chen, Ben and Lei, Chenyi and Ou, Wenwu},
  journal={arXiv preprint arXiv:2605.18434},
  year={2026}
}