ConClick-Data / README.md
librarian-bot's picture
Librarian Bot: Add language metadata for dataset
cc98eb3 verified
|
Raw History Blame
2.74 kB
---
language:
- en
license: other
size_categories:
- 1K<n<10K
task_categories:
- image-to-text
pretty_name: ClickContrast
license_name: upstream-source-terms
license_link: https://huggingface.co/datasets/mlfoundations/Click-100k
tags:
- gui-grounding
- computer-use
- contrastive-learning
configs:
- config_name: default
data_files:
- split: all_pairs
path: data/all_pairs.jsonl
- split: all_replay
path: data/all_replay.jsonl
---
# ClickContrast
ClickContrast contains **4,292 target–distractor pairs and 4,292 coverage examples** from [Click-100k](https://huggingface.co/datasets/mlfoundations/Click-100k), with 6,690 distinct screenshots.
It is the training dataset for [SelectGround-8B](https://huggingface.co/ruotian/SelectGround-8B).
| File | Rows | Supervision |
|---|---:|---|
| all_pairs | 4,292 | Target coordinate, target region, distractor region, candidate regions |
| all_replay | 4,292 | Target coordinate |
Training uses `all_pairs` and `all_replay` with a 1:1 pair-to-coverage sampling cycle.
Pairs retain teacher-supported targets together with a model prediction on another parsed element outside a centered 2x target neighborhood.
A focused subset covers teacher-checked small controls and icons.
Coverage examples span 86 Click100k source categories.
## Format
Download the entire repository to retain the relative screenshot paths:
~~~bash
hf download ruotian/ClickContrast --repo-type dataset --revision paper --local-dir data/clickcontrast
~~~
Each row contains:
- **id**: stable example identifier.
- **image**: screenshot path relative to this repository.
- **image_width**, **image_height**, **image_sha256**: original dimensions and file checksum.
- **instruction**: original grounding instruction.
- **response**: target center as [x,y], with integer coordinates normalized to 0–1000.
- **source**: upstream dataset, split, and row index.
Paired rows additionally contain **target_bbox**, **distractor_bbox**, and **candidate_bboxes**, all in original-image pixels with [left,top,right,bottom] order.
The JSONL order is part of the frozen training recipe. The manifest records file hashes and counts.
## Training
Use the SFT-plus-selection-loss recipe in the [SelectGround code repository](https://github.com/zhangruotian/SelectGround).
All examples contribute coordinate loss. Only paired examples contribute selection loss.
No external parser or teacher is needed to train from this release.
## Source terms
Screenshots are a subset of Click-100k and retain their upstream provenance.
They are not relicensed as project code. Use is subject to the applicable terms of Click-100k and its constituent sources.
The repository's code license does not replace those data terms.