Datasets:

ruotian
/

ContrastGround

pretty_name: ContrastGround
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: data/corpus/train.jsonl
  - config_name: selectground-8b
    data_files:
      - split: train_pairs
        path: data/selectground-8b/train_pairs.jsonl
      - split: train_replay
        path: data/selectground-8b/train_replays.jsonl
  - config_name: selectground-30b-a3b
    data_files:
      - split: train_pairs
        path: data/selectground-30b-a3b/train_pairs.jsonl
      - split: train_replay
        path: data/selectground-30b-a3b/train_replays.jsonl
task_categories:
  - image-to-text
tags:
  - gui-grounding
  - computer-use
  - contrastive-learning
  - sft
license: other

ContrastGround

ContrastGround is the training corpus for the updated SelectGround-8B and SelectGround-30B-A3B checkpoints. It combines UI-Vision-cleaned ClickContrast examples with teacher-verified hard negatives mined from GroundCUA. It does not contain UI-Vision examples.

Which configuration should I use?

Configuration	Pair rows	Replay rows	Released model
`selectground-8b`	4,942	4,103	`ruotian/SelectGround-8B`
`selectground-30b-a3b`	4,544	3,824	`ruotian/SelectGround-30B-A3B`
`corpus`	4,944	4,103	Auditable union of both recipes

The two model configurations are intentionally different. Use the exact configuration named by the model card to reproduce that checkpoint. The corpus rows include training_configs, which records membership in one or both released recipes.

from datasets import load_dataset

dataset = load_dataset("ruotian/ContrastGround", "selectground-8b")
pairs = dataset["train_pairs"]
replay = dataset["train_replay"]

The image field is a repository-relative content-addressed path. When using a snapshot checkout, resolve it relative to the snapshot root.

Schema

Every row contains image, image_width, image_height, instruction, response, target_bbox, source_ref, role, and source_license. Pair rows also contain distractor_bbox and candidate_bboxes, which supervise the SelectGround auxiliary selection loss. Coordinates in bounding boxes are absolute source-image pixels; response is the target center in normalized 0–1000 coordinates.

Construction

The ClickContrast component starts from Click-100k and removes all examples whose canonical source is UI-Vision (element_grounding or layout_grounding). Its replay remains stratified across the retained Click-100k sources.

The GroundCUA component uses two natural-language views per target. A target is eligible only when MAI-UI-8B and UI-Venus-1.5-8B both land inside the original pixel-space ground-truth box, while Qwen3-VL-8B misses the expanded target neighborhood and lands on a different native UI element. Spatial prompts also require a unique text anchor and an unambiguous nearest target. The released views differ only in how conservatively those GroundCUA pairs and replay rows are sampled.

Pinned upstream revisions and SHA-256 checksums for every JSONL are recorded in manifest.json. Images are named by their byte-level SHA-256 digest; 8,423 source paths collapse to 6,834 distinct image files.

Licensing and provenance

GroundCUA is distributed under MIT by ServiceNow. At the pinned revision used here, the Click-100k dataset card does not declare a dataset-level license. Accordingly this mixed-source dataset uses license: other, and every row records its source and source-license status. Users are responsible for checking the upstream terms for their intended use. This card does not relicense upstream images or annotations.

The optional teacher-point provenance fields inside source_ref are JSON-encoded coordinate strings so that Click-100k and GroundCUA rows share a stable Arrow schema; they are not training targets.

Evaluation leakage

The ClickContrast component was rebuilt to exclude UI-Vision provenance before training. ScreenSpot-Pro, UI-Vision, and OSWorld-G are not validation splits of this dataset. Reported released checkpoints were selected using those public benchmarks, so their scores should be treated as test-tuned rather than as held-out model-selection estimates.