---
pretty_name: DRIVE-UGC
language:
  - zh
size_categories:
  - 10K<n<100K
tags:
  - automotive
  - multimodal
  - recommendation
  - ranking
  - user-generated-content
  - precomputed-features
configs:
  - config_name: events
    default: true
    data_files:
      - split: B_train
        path: records/B_train.jsonl
      - split: A_train
        path: records/A_train.jsonl
      - split: A_val
        path: records/A_val.jsonl
      - split: A_test
        path: records/A_test.jsonl
---

# DRIVE-UGC

**Decision-stage Ranking with Image and Verbal Evidence from User-Generated Content** is a Chinese automotive benchmark for ranking 16 candidate vehicles from collections of user posts and images. It contains 15,850 events, 89,274 posts, and 105,241 selected-image references. The image archive contains 105,043 unique images, identified by content hashes.

The release provides research-processed text, selected original image bytes, event and content-group metadata, and frozen post and image-region features. It supports the cross-stage ranking task used by CAMPER, including bottleneck group-wise feature purification (BGFP) and relative region purification (RRP).

## Source and stage definitions

The research collection was assembled for a 2026 study from publicly accessible Autohome automotive-community contributions. An event groups a user's retained posts and associated selected images with one recorded target-vehicle label.

Stages follow observable evidence of target-vehicle choice or ownership. Target events contain eligible posts preceding the selected evidence post. Source events contain retained choice, ownership, or use evidence when eligible target input is absent. These operational stages describe the evidence available in the collection.

## Partitions

| Partition | Role | All events | Paired events | Posts | Image references |
| --- | --- | ---: | ---: | ---: | ---: |
| B_train | Source training | 9,080 | 8,786 | 18,794 | 59,854 |
| A_train | Target training | 4,189 | 3,343 | 37,789 | 25,469 |
| A_val | Target validation | 823 | 823 | 11,554 | 6,367 |
| A_test | Target test | 1,758 | 1,758 | 21,137 | 13,551 |
| Total | | 15,850 | 14,710 | 89,274 | 105,241 |

The complete benchmark includes 1,140 events without images. The matched multimodal experiments use the **paired subset**, selected by the metadata field `paired`. Validation and test are entirely paired. Filtering to paired events preserves each event's original public index and feature offsets.

Event identifiers, content-group identifiers, labels, and partition assignments retain their archived associations. Content groups link related records; retain these groups when sampling training subsets. `labels.json` maps integer labels 0–15 to the 16 Chinese vehicle names.

## Released text, images, and features

**Text.** The records contain the archived, sanitized title and body of each retained post. Vehicle, brand, and trim expressions are represented by the research preprocessing placeholders. Release preparation additionally replaced 14 contact strings across nine posts with `[CONTACT]`: 11 explicit mobile contact numbers, one QQ identifier, and two phone-number-formatted usernames; the frozen features retain their original encoding, so these nine released texts differ from the encoded texts by those replacements. Posts retain their archived order within each event, aligned with the event's text-feature rows.

**Images.** TAR shards contain the selected original image bytes from the research archive. The image index maps each content hash to its shard and member path. Event records reference these images by hash and path; they do not embed image bytes.

**Text features.** `BAAI/bge-large-zh-v1.5` supplies one L2-normalized, 1,024-dimensional post vector, stored as float32. Each post is encoded from its sanitized title and body with a 512-token limit.

**Visual features.** `google/siglip2-so400m-patch14-384` supplies the **OCR-MASK regional features** used by the current BGFP/RRP method. Each image contributes 81 normalized region vectors arranged as a 9×9 grid, with 1,152 coordinates stored as float16. These come from averaging non-overlapping 3×3 neighborhoods of the encoder's 27×27 patch grid.

The selected original images and OCR-MASK features are distinct release components: the image archive preserves selected source bytes, while the feature arrays reflect the benchmark's detected vehicle-identity-text masking procedure. The arrays retain region structure for RRP aggregation. Encoder weights are obtained from their upstream repositories.

Vehicle-identity masking targets textual cues for vehicle ranking. Selected original images may contain recognizable people, license plates, logos, and other identifying details. Public event IDs describe the metadata representation and do not establish complete anonymity of source content. See [SOURCE_NOTICE.md](SOURCE_NOTICE.md) for provenance and rights.

## Repository layout

```text
README.md
SOURCE_NOTICE.md
MANIFEST.json
labels.json
statistics.json
records/
  B_train.jsonl
  A_train.jsonl
  A_val.jsonl
  A_test.jsonl
splits/{split}/
  events.csv
  events.npz
features/{split}/
  text.npy
  text_event_offsets.npy
  regions.npy
  region_event_offsets.npy
  region_image_offsets.npy
images/
  image_index.jsonl
  images-00000.tar
  images-00001.tar
  ...
code/
  load_features.py
```

The four `split` names are `B_train`, `A_train`, `A_val`, and `A_test`. Image shards are approximately 512 MiB each, with a smaller final shard. Feature arrays occupy approximately 20 GB in total. Download only the partitions and image shards needed for an analysis. `MANIFEST.json` provides file sizes and SHA-256 hashes for integrity verification.

### Metadata and offsets

`events.csv` contains `event_id`, `content_group_id`, `label`, `paired`, `post_count`, and `image_count`, in public event order. `events.npz` stores the aligned arrays `ids`, `groups`, `y`, and `paired`.

For event index `i`, `text_event_offsets[i:i+2]` gives its half-open post-row interval in `text.npy`. `region_event_offsets[i:i+2]` gives its region-row interval in `regions.npy`. `region_image_offsets.npy` preserves image boundaries within the concatenated region array. Events with no images have an empty region interval.

Each JSONL record contains `event_id`, `group_id`, `label`, `paired`, `posts`, and `images`. The record's `group_id` corresponds to `content_group_id` in the CSV and feature loader. Each post has `sanitized_title` and `sanitized_text`; each image reference has `path`, `sha256`, `width`, `height`, and `post_index`. Event and within-event ordering preserve the archived feature alignment.

`images/image_index.jsonl` contains `sha256`, `path`, `archive`, and `bytes` for each unique image. Image paths have the form `images/<hash-prefix>/<sha256>.<extension>` and match the member names inside their TAR archive. The Hub configuration selects only `records/{split}.jsonl` for the viewer; label mappings, statistics, feature arrays, and TAR files remain separate assets. Image references are paths and hashes rather than decoded image columns. This follows the Hub's [explicit data-file configuration](https://huggingface.co/docs/hub/datasets-data-files-configuration).

## Load a downloaded partition

Download `features/A_test/`, `splits/A_test/`, `labels.json`, and `code/load_features.py` into the repository layout above. Run from a directory containing the downloaded repository:

```python
from pathlib import Path
import sys

root = Path("DRIVE-UGC").resolve()
sys.path.insert(0, str(root / "code"))
from load_features import load_split

ds = load_split(root, "A_test")
event = ds.event(int(ds.paired_indices[0]))

print(event["event_id"], event["label"])
print(event["posts"].shape)    # (number of posts, 1024)
print(event["regions"].shape)  # (81 * number of images, 1152)

boundaries = event["image_region_offsets"]
first_image_regions = event["regions"][boundaries[0]:boundaries[1]]
```

The loader returns `public_event_index`, `event_id`, `content_group_id`, `label`, `paired`, `posts`, `regions`, and event-relative `image_region_offsets`. Use the public event index when joining feature slices to metadata; paired-subset positions are a different indexing space.

To inspect local text records:

```python
import json

with (root / "records" / "A_test.jsonl").open(encoding="utf-8") as f:
    record = json.loads(next(f))
print(record.keys())
```

For original images, resolve the record's image hash through the image index, then read the named member from its TAR shard:

```python
import tarfile

with (root / "images" / "image_index.jsonl").open(encoding="utf-8") as f:
    image_index = {item["sha256"]: item for item in map(json.loads, f)}

image_ref = record["images"][0]  # A_test events all contain images
entry = image_index[image_ref["sha256"]]
with tarfile.open(root / entry["archive"], "r") as archive:
    with archive.extractfile(entry["path"]) as image_file:
        image_bytes = image_file.read()
```

This retrieves selected source-image bytes. The released regional arrays provide the corresponding OCR-MASK model inputs.

## Attribution and rights

Source contributions originate from [Autohome](https://www.autohome.com.cn/). Text features use [BGE-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5), and regional features use [SigLIP 2 SO400M patch14-384](https://huggingface.co/google/siglip2-so400m-patch14-384). Refer to the upstream model cards for encoder documentation and terms.

Third-party posts and images remain subject to the rights of their original rights holders. This repository grants no additional open-content license for those source materials. [SOURCE_NOTICE.md](SOURCE_NOTICE.md) describes the released transformations and source rights.