Enhance dataset card: Add metadata, detailed description, and sample usage (#1)

- Enhance dataset card: Add metadata, detailed description, and sample usage (e0bfa9b3149907feb1219611e579491b22a9ecaf)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,48 @@
+---
+task_categories:
+  - image-text-to-text
+  - video-text-to-text
+  - object-detection
+  - image-segmentation
+language:
+  - en
+---
+
 This repository contains the evaluation data presented in: [OneThinker: All-in-one Reasoning Model for Image and Video](https://arxiv.org/abs/2512.03043)
 
-Code: https://github.com/tulerfeng/OneThinker
+Project Page: https://github.com/tulerfeng/OneThinker
+Code: https://github.com/tulerfeng/OneThinker
+
+## About OneThinker
+
+<div align="center">
+  <img src="https://github.com/tulerfeng/OneThinker/blob/main/assets/teaser.png?raw=true" alt="OneThinker Teaser" width="95%">
+</div>
+
+We introduce **OneThinker**, an all-in-one multimodal reasoning generalist that is **capable of thinking across a wide range of fundamental visual tasks within a single model**.
+
+We construct the large-scale **OneThinker-600k** multi-task training corpus and build **OneThinker-SFT-340k** with high-quality CoT annotations for cold-start SFT. Moreover, we propose **EMA-GRPO**, a new RL method that **balances heterogeneous reward signals across diverse visual tasks**, via simply tracking task-wise moving averages of reward std.
+
+OneThinker demonstrates **strong performance on 31 benchmarks across 10 fundamental vision tasks**, while showing cross-task knowledge transfer and promising zero-shot generalization toward a **unified multimodal reasoning generalist**.
+
+All code, models, and data are fully released.
+
+## Dataset
+
+Our dataset covers both image and video modalities and spans a series of fundamental visual reasoning tasks, including rule-based QA, open-ended QA, captioning, spatial grounding, temporal grounding, spatio-temporal grounding, tracking, and segmentation
+
+<div align="center">
+  <img src="https://github.com/tulerfeng/OneThinker/blob/main/assets/dataset.png?raw=true" alt="OneThinker Dataset Overview" width="90%">
+</div>
+
+To enable effective SFT initialization for reasoning, we leverage a strong proprietary model, Seed1.5-VL to produce CoT annotations.
+
+The `onethinker_rl_train.json` file is for RL training while `onethinker_sft_image.json` and `onethinker_sft_video.json` is for SFT cold start. The json files end with `_unsampled` are unsampled full set.
+
+## Sample Usage
+
+For inference on a single example, you may refer to:
+
+```bash
+python ./Evaluation/inference_single/inference.py
+```