# VisionAI DataVerse

## English

- [Meet DataVerse](https://linkervision.gitbook.io/dataverse/readme.md): Rapidly Construct Your AI Models with Data.
- [Creating Your First Project](https://linkervision.gitbook.io/dataverse/data-management/creating-your-first-project.md): Flexible configuration of the ontology required for the project.
- [Import Your Dataset](https://linkervision.gitbook.io/dataverse/data-management/import-your-dataset.md): Dataverse streamlines your data management processes, making it easier to organize and visualize your data.
- [Data Slice - Specific Subsets](https://linkervision.gitbook.io/dataverse/data-management/data-slice-specific-subsets.md): Create endless possibilities for model training, validation, and testing with Data Slices.
- [Data Visualization](https://linkervision.gitbook.io/dataverse/data-management/data-visualization.md): Revolutionize your data management and selection with our intuitive and powerful data visualization tools
- [Image](https://linkervision.gitbook.io/dataverse/data-management/data-visualization/image.md)
- [Point Cloud](https://linkervision.gitbook.io/dataverse/data-management/data-visualization/point-cloud.md)
- [Frame View](https://linkervision.gitbook.io/dataverse/data-management/data-visualization/frame-view.md)
- [Sequence View](https://linkervision.gitbook.io/dataverse/data-management/data-visualization/sequence-view.md)
- [\<Use Case> Clean Raw Data](https://linkervision.gitbook.io/dataverse/data-management/data-visualization/less-than-use-case-greater-than-clean-raw-data.md): Sampling data that have the highest impact on model training
- [\<Use Case> Find More Rare Cases](https://linkervision.gitbook.io/dataverse/data-management/data-visualization/less-than-use-case-greater-than-find-more-rare-cases.md): Easily identify rare cases and find more training data
- [\<Use Case> Identify Model Weakness](https://linkervision.gitbook.io/dataverse/data-management/data-visualization/less-than-use-case-greater-than-identify-model-weakness.md): Find model weakness with ground truth
- [Data Metrics](https://linkervision.gitbook.io/dataverse/data-management/data-metrics.md): Empower your insights with data-driven chart analysis
- [Image Quality Assessment (IQA)](https://linkervision.gitbook.io/dataverse/advanced-data-features/image-quality-assessment-iqa.md): IQA helps you analyze and clean up images for better quality and performance.
- [Auto-Tagging](https://linkervision.gitbook.io/dataverse/advanced-data-features/auto-tagging.md)
- [Data Discovery (Beta)](https://linkervision.gitbook.io/dataverse/advanced-data-features/data-discovery-beta.md): Streamlines your image search process by allowing text typing, quickly find specific images from your dataset.
- [Data Sampling](https://linkervision.gitbook.io/dataverse/advanced-data-features/data-sampling.md): Sampling Techniques in DataVerse for Data Slices
- [Data Splitting](https://linkervision.gitbook.io/dataverse/advanced-data-features/data-splitting.md)
- [Data Query](https://linkervision.gitbook.io/dataverse/advanced-data-features/data-query.md): The custom query feature allowing you to specify and filter according to your unique project requirements.
- [Element](https://linkervision.gitbook.io/dataverse/advanced-data-features/data-query/element.md): The target of the search query
- [Logic](https://linkervision.gitbook.io/dataverse/advanced-data-features/data-query/logic.md): Connect search elements and further refine the search query.
- [Use Cases](https://linkervision.gitbook.io/dataverse/advanced-data-features/data-query/use-cases.md): use cases of custom data query
- [Before Starting Annotation Task...](https://linkervision.gitbook.io/dataverse/annotation/before-starting-annotation-task....md)
- [Create Annotation Task](https://linkervision.gitbook.io/dataverse/annotation/create-annotation-task.md)
- [Task Overview](https://linkervision.gitbook.io/dataverse/annotation/task-overview.md)
- [Manpower](https://linkervision.gitbook.io/dataverse/annotation/manpower.md)
- [Labeling/Reviewing Panel](https://linkervision.gitbook.io/dataverse/annotation/labeling-reviewing-panel.md)
- [VQA Labeling Panel](https://linkervision.gitbook.io/dataverse/annotation/labeling-reviewing-panel/vqa-labeling-panel.md)
- [Statistics](https://linkervision.gitbook.io/dataverse/annotation/statistics.md)
- [Detail](https://linkervision.gitbook.io/dataverse/annotation/detail.md)
- [Train Your AI Model](https://linkervision.gitbook.io/dataverse/model-training-and-evaluation/train-your-ai-model.md): Unleash the Power of Visual AI for Your Business
- [Model Performance](https://linkervision.gitbook.io/dataverse/model-training-and-evaluation/model-performance.md)
- [Prediction](https://linkervision.gitbook.io/dataverse/model-training-and-evaluation/prediction.md): Leveraging Prediction Functionality in Dataverse
- [Model Convert (Beta)](https://linkervision.gitbook.io/dataverse/model-training-and-evaluation/model-convert-beta.md): Optimize their AI models for various deployment environments.
- [Model Download (Beta)](https://linkervision.gitbook.io/dataverse/model-training-and-evaluation/model-download-beta.md)
- [VQA Prediction & Evaluation](https://linkervision.gitbook.io/dataverse/model-training-and-evaluation/vqa-prediction-and-evaluation.md)
- [VisionAI Data Format](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format.md)
- [coordinate\_systems](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/coordinate_systems.md)
- [streams](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/streams.md)
- [contexts](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/contexts.md)
- [objects](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/objects.md)
- [frames](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frames.md)
- [objects](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frames/objects.md)
- [bbox](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frames/objects/bbox.md)
- [cuboid](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frames/objects/cuboid.md)
- [poly2d](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frames/objects/poly2d.md)
- [point](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frames/objects/point.md)
- [binary](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frames/objects/binary.md)
- [contexts](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frames/contexts.md)
- [attributes](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frames/attributes.md)
- [frame\_interval](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/frame_interval.md)
- [tags](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/tags.md)
- [metadata](https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format/metadata.md): The version of the format.
- [Use Case](https://linkervision.gitbook.io/dataverse/visionai-format/use-case.md)
- [bbox](https://linkervision.gitbook.io/dataverse/visionai-format/use-case/bbox.md): bbox use case example
- [polygon](https://linkervision.gitbook.io/dataverse/visionai-format/use-case/polygon.md): polygon use case example
- [polyline](https://linkervision.gitbook.io/dataverse/visionai-format/use-case/polyline.md): polyline use case example
- [point](https://linkervision.gitbook.io/dataverse/visionai-format/use-case/point.md): point use case example
- [semantic segmetation](https://linkervision.gitbook.io/dataverse/visionai-format/use-case/semantic-segmetation.md): semantic segmetation use case example
- [classification](https://linkervision.gitbook.io/dataverse/visionai-format/use-case/classification.md): classification use case example
- [bbox + cuboid (3d)](https://linkervision.gitbook.io/dataverse/visionai-format/use-case/bbox-+-cuboid-3d.md): bbox + cuboid use case example
- [tagging](https://linkervision.gitbook.io/dataverse/visionai-format/use-case/tagging.md): tagging use case example
- [Format FQA](https://linkervision.gitbook.io/dataverse/visionai-format/format-fqa.md)
- [VLM Data Format (VQA)](https://linkervision.gitbook.io/dataverse/visionai-format/vlm-data-format-vqa.md): Vision-Language Model (VLM) input and output format.
- [Appendix: Training Format](https://linkervision.gitbook.io/dataverse/visionai-format/appendix-training-format.md)
- [Usage and Billing](https://linkervision.gitbook.io/dataverse/dataverse-usage/usage-and-billing.md)
- [Release 2026/5/27](https://linkervision.gitbook.io/dataverse/updates/release-2026-5-27.md)
- [Release 2026/2/4](https://linkervision.gitbook.io/dataverse/updates/release-2026-2-4.md)
- [Release 2025/12/16](https://linkervision.gitbook.io/dataverse/updates/release-2025-12-16.md)
- [Release 2025/8/25](https://linkervision.gitbook.io/dataverse/updates/release-2025-8-25.md)
- [Release 2025/6/10](https://linkervision.gitbook.io/dataverse/updates/release-2025-6-10.md)
- [Release 2025/4/10](https://linkervision.gitbook.io/dataverse/updates/release-2025-4-10.md)
- [Release 2025/1/8](https://linkervision.gitbook.io/dataverse/updates/release-2025-1-8.md)
- [Release 2024/11/12](https://linkervision.gitbook.io/dataverse/updates/release-2024-11-12.md)
- [Release 2024/09/18](https://linkervision.gitbook.io/dataverse/updates/release-2024-09-18.md)
- [Release 2024/08/06](https://linkervision.gitbook.io/dataverse/updates/release-2024-08-06.md)
- [Initial Release 2024/01/01](https://linkervision.gitbook.io/dataverse/updates/initial-release-2024-01-01.md)

## 繁體中文

- [認識 DataVerse](https://linkervision.gitbook.io/dataverse/traditional-chinese/readme.md): VisionAI DataVerse - 資料快速建構您的 AI 模型
- [建立您的第一個AI專案](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/jian-li-nin-de-di-yi-ge-ai-zhuan-an.md): 靈活配置您專案所需的各種設定
- [匯入您的資料](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/hui-ru-nin-de-zi-liao.md): DataVerse簡化了資料管理流程，使您更容易組織和視覺化您的資料。
- [組合專屬 Data Slice - 資料子集](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/zu-he-zhuan-shu-data-slice-zi-liao-zi-ji.md): 資料子集組合使用Data Slice為模型訓練、驗證和測試創建無窮的可能性。
- [觀察及挑選資料 Data Visualization](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/guan-cha-ji-tiao-xuan-zi-liao-data-visualization.md): 透過直觀且強大的資料視覺化工具，讓您的資料管理和選擇革命性地升級。
- [圖片資料](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/guan-cha-ji-tiao-xuan-zi-liao-data-visualization/tu-pian-zi-liao.md): 圖片資料
- [Point Cloud 點雲圖](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/guan-cha-ji-tiao-xuan-zi-liao-data-visualization/point-cloud-dian-yun-tu.md): 點雲圖
- [Frame 視角](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/guan-cha-ji-tiao-xuan-zi-liao-data-visualization/frame-shi-jiao.md)
- [Sequence 視角](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/guan-cha-ji-tiao-xuan-zi-liao-data-visualization/sequence-shi-jiao.md)
- [<使用範例> 清理資料](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/guan-cha-ji-tiao-xuan-zi-liao-data-visualization/shi-yong-fan-li-qing-li-zi-liao.md): Sampling data that have the highest impact on model training
- [<使用範例> 找出罕見資料](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/guan-cha-ji-tiao-xuan-zi-liao-data-visualization/shi-yong-fan-li-zhao-chu-han-jian-zi-liao.md): Easily identify rare cases and find more training data
- [<使用範例> 辨識模型弱點](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/guan-cha-ji-tiao-xuan-zi-liao-data-visualization/shi-yong-fan-li-bian-shi-mo-xing-ruo-dian.md): Find model weakness with ground truth
- [查看分析圖表](https://linkervision.gitbook.io/dataverse/traditional-chinese/guan-li-nin-de-zi-liao/cha-kan-fen-xi-tu-biao.md): 以數據圖表分析賦予您更深入的見解
- [影像品質評估 IQA](https://linkervision.gitbook.io/dataverse/traditional-chinese/jin-jie-zi-liao-gong-neng/ying-xiang-pin-zhi-ping-gu-iqa.md): IQA 協助您分析影像品質，快速找出目標，加快清理影像的效率。
- [自動標籤 Auto-Tagging](https://linkervision.gitbook.io/dataverse/traditional-chinese/jin-jie-zi-liao-gong-neng/zi-dong-biao-qian-autotagging.md)
- [資料探索發現 Data Discovery (Beta)](https://linkervision.gitbook.io/dataverse/traditional-chinese/jin-jie-zi-liao-gong-neng/zi-liao-tan-suo-fa-xian-data-discovery-beta.md): 通過輸入文字來簡化您的圖像搜索過程，加速找到未標註的特定的圖片。
- [資料取樣 Data Sampling](https://linkervision.gitbook.io/dataverse/traditional-chinese/jin-jie-zi-liao-gong-neng/zi-liao-qu-yang-data-sampling.md): 提供各種取樣技術，讓您在大量資料海中快速縮減樣本。
- [資料切割 Data Spliting](https://linkervision.gitbook.io/dataverse/traditional-chinese/jin-jie-zi-liao-gong-neng/zi-liao-qie-ge-data-spliting.md): 根據各種情況來切割您所想要的data slice
- [創建標註任務 Create Annotation Task](https://linkervision.gitbook.io/dataverse/traditional-chinese/zi-liao-biao-zhu/chuang-jian-biao-zhu-ren-wu-create-annotation-task.md)
- [標註任務概覽 (Tab "Overview")](https://linkervision.gitbook.io/dataverse/traditional-chinese/zi-liao-biao-zhu/biao-zhu-ren-wu-gai-lan-tab-overview.md): 標註任務概覽
- [指派標註員/審查員 (Tab "Manpower")](https://linkervision.gitbook.io/dataverse/traditional-chinese/zi-liao-biao-zhu/zhi-pai-biao-zhu-yuan-shen-cha-yuan-tab-manpower.md)
- [標註面板與審查面板](https://linkervision.gitbook.io/dataverse/traditional-chinese/zi-liao-biao-zhu/biao-zhu-mian-ban-yu-shen-cha-mian-ban.md): 標註面板與審查面板
- [VQA 標註面板](https://linkervision.gitbook.io/dataverse/traditional-chinese/zi-liao-biao-zhu/biao-zhu-mian-ban-yu-shen-cha-mian-ban/vqa-biao-zhu-mian-ban.md)
- [數據統計(Tab "Statistics")](https://linkervision.gitbook.io/dataverse/traditional-chinese/zi-liao-biao-zhu/shu-ju-tong-ji-tab-statistics.md)
- [標注流程、檔案狀態與標註員/審查員的關係](https://linkervision.gitbook.io/dataverse/traditional-chinese/zi-liao-biao-zhu/biao-zhu-liu-cheng-dang-an-zhuang-tai-yu-biao-zhu-yuan-shen-cha-yuan-de-guan-xi.md)
- [檔案頁面 (Tab "Files")](https://linkervision.gitbook.io/dataverse/traditional-chinese/zi-liao-biao-zhu/dang-an-ye-mian-tab-files.md): 檔案頁面
- [訓練您的AI模型](https://linkervision.gitbook.io/dataverse/traditional-chinese/mo-xing-xun-lian/xun-lian-nin-de-ai-mo-xing.md): Unleash the Power of Visual AI for Your Business
- [深入了解您的模型表現](https://linkervision.gitbook.io/dataverse/traditional-chinese/mo-xing-xun-lian/shen-ru-liao-jie-nin-de-mo-xing-biao-xian.md): 了解您的模型，並隨時調整資料與訓練策略了解深入
- [使用模型預測功能](https://linkervision.gitbook.io/dataverse/traditional-chinese/mo-xing-xun-lian/shi-yong-mo-xing-yu-ce-gong-neng.md): 利用Prediction來比較不同模型在同一組資料上的表現，快速調整模型策略
- [模型轉換 (Beta)](https://linkervision.gitbook.io/dataverse/traditional-chinese/mo-xing-xun-lian/mo-xing-zhuan-huan-beta.md): 最佳化 AI 模型以適應不同的部署環境
- [模型下載 (Beta)](https://linkervision.gitbook.io/dataverse/traditional-chinese/mo-xing-xun-lian/mo-xing-xia-zai-beta.md): 模型下載
- [VLM 資料格式 (VQA)](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/vlm-zi-liao-ge-shi-vqa.md): 視覺-語言模型 (VLM) 的輸入與輸出格式
- [使用範例](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/shi-yong-fan-li.md): 使用範例
- [bbox](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/shi-yong-fan-li/bbox.md): bbox use case example
- [polygon](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/shi-yong-fan-li/polygon.md): polygon use case example
- [polyline](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/shi-yong-fan-li/polyline.md): polyline use case example
- [point](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/shi-yong-fan-li/point.md): point use case example
- [semantic segmetation](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/shi-yong-fan-li/semantic-segmetation.md): semantic segmetation use case example
- [classification](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/shi-yong-fan-li/classification.md): classification use case example
- [bbox + cuboid (3d)](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/shi-yong-fan-li/bbox-+-cuboid-3d.md): bbox + cuboid use case example
- [tagging](https://linkervision.gitbook.io/dataverse/traditional-chinese/visionai-format-zi-liao-ge-shi/shi-yong-fan-li/tagging.md): tagging use case example
- [問題集](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji.md): 問題集
- [Data Slice的作用是甚麼?](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/data-slice-de-zuo-yong-shi-shen-mo.md)
- [可以跨Project使用Dataset、Data slice嗎?](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/ke-yi-kua-project-shi-yong-datasetdata-slice-ma.md)
- [Training Job ，Training Pipeline和Model name是什麼？](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/training-job-training-pipeline-he-model-name-shi-shen-mo.md)
- [Model Training的時候 model 裏的 class 選項的勾選取消會怎麽樣？](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/model-training-de-shi-hou-model-li-de-class-xuan-xiang-de-gou-xuan-qu-xiao-hui-zen-mo-yang.md)
- [選dataslice的時候，可以只取dataset的某一種class的一千張嗎?](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/xuan-dataslice-de-shi-hou-ke-yi-zhi-qu-dataset-de-mou-yi-zhong-class-de-yi-qian-zhang-ma.md)
- [VisionAI平臺在資料量大時候，訓練時顯示預估時間特別久，這樣正常嗎?](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/visionai-ping-tai-zai-zi-liao-liang-da-shi-hou-xun-lian-shi-xian-shi-yu-gu-shi-jian-te-bie-jiu-zhe-y.md)
- [VisionAI和coco格式的標註可以混用嗎？](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/visionai-he-coco-ge-shi-de-biao-zhu-ke-yi-hun-yong-ma.md)
- [怎樣刪除Data Slice？](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/zen-yang-shan-chu-data-slice.md)
- [怎樣刪除不用的Dataset？](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/zen-yang-shan-chu-bu-yong-de-dataset.md)
- [項目中的Tag和IQA的作用？](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/xiang-mu-zhong-de-tag-he-iqa-de-zuo-yong.md): 項目
- [從其它專案導入資料集時失敗](https://linkervision.gitbook.io/dataverse/traditional-chinese/zhi-yuan/wen-ti-ji/cong-qi-ta-zhuan-an-dao-ru-zi-liao-ji-shi-shi-bai.md)
- [版本更新 2026/2/4](https://linkervision.gitbook.io/dataverse/traditional-chinese/update/ban-ben-geng-xin-202624.md)
- [版本更新 2025/12/16](https://linkervision.gitbook.io/dataverse/traditional-chinese/update/ban-ben-geng-xin-20251216.md): （目標）
- [版本更新 2025/8/25](https://linkervision.gitbook.io/dataverse/traditional-chinese/update/ban-ben-geng-xin-2025825.md)
- [版本更新 2025/6/10](https://linkervision.gitbook.io/dataverse/traditional-chinese/update/release-2025-6-10.md)