sankaku_tags_categorize_for_WD14Tagger
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# Sankaku Tags Categorize for WD14Tagger ## Summary This dataset provides a comprehensive **tag categorization system** specifically designed for enhancing the WD14Tagger model with Sankaku Complex metadata. The dataset contains **10,861 meticulously classified tags** from the Sankaku Complex platform, organized into distinct categories to improve tag-based image classification and generation tasks. Each tag entry includes the original Sankaku tag ID, tag name, assigned category, and usage count, enabling fine-grained control over tag processing in image recognition pipelines. The **categorical organization** follows a structured approach where tags are grouped into meaningful classes that align with common image annotation requirements. This classification system allows for more intelligent tag filtering, prioritization, and processing in automated image tagging systems. The dataset serves as a **bridge between Sankaku Complex's extensive tag ecosystem** and the WD14Tagger framework, providing standardized category mappings that enhance model performance and interpretability. With **detailed metadata** including usage frequency statistics, this dataset supports both training and inference phases of image tagging models. The category assignments enable selective tag processing based on semantic relevance, reducing noise and improving the quality of generated tags. This is particularly valuable for **content creators and researchers** working with anime-style artwork who require precise and organized tag management for their image datasets. The dataset's structure facilitates **seamless integration** with existing WD14Tagger workflows while adding the rich tag taxonomy of Sankaku Complex. By providing category-level organization alongside individual tag data, this resource enables more sophisticated tag-based image retrieval, classification, and generation applications in the computer vision domain. Keywords: **tag-categorization**, **image-annotation**, **metadata-enhancement**, **WD14Tagger**, **Sankaku-Complex** ## Dataset Structure The dataset consists of a single CSV file with the following schema: - `tag_id`: Unique identifier from Sankaku Complex - `name`: The actual tag text/name - `category`: Numerical category assignment (0, 3, etc.) - `count`: Usage frequency count on Sankaku Complex ### Sample Data | tag_id | name | category | count | |--------|------|----------|-------| | 478554 | contrast | 0 | 850 | | 2075818 | nike_(company) | 3 | 1884 | | 2068712 | hugging_book | 3 | 870 | | 2035712 | rhodes_island_logo_(arknights) | 3 | 976 | | 2006775 | unworn_goggles | 3 | 699 | ## Usage ```python import pandas as pd # Load the dataset df = pd.read_csv('selected_tags_classified.csv') # Explore the data print(f"Total tags: {len(df)}") print(f"Categories: {df['category'].unique()}") print(f"Sample tags from category 0:") print(df[df['category'] == 0].head()) # Get most frequent tags top_tags = df.sort_values('count', ascending=False).head(10) print("Most popular tags:") print(top_tags[['name', 'category', 'count']]) ``` ## Integration with WD14Tagger This dataset can be used to enhance WD14Tagger by providing category-based tag filtering and organization: ```python # Example: Filter tags by category for specific use cases def get_tags_by_category(df, category_ids): return df[df['category'].isin(category_ids)] # Get all tags from specific categories character_tags = get_tags_by_category(df, [3]) # Example category for character/object tags ``` ## Original Content The dataset contains 10,861 classified tags from Sankaku Complex with the following characteristics: - Total rows: 10,861 - File size: 326,595 bytes - Columns: tag_id, name, category, count - Categories represented: Multiple numerical categories (0, 3, etc.) ## Citation ```bibtex @misc{sankaku_tags_categorize_wd14tagger, title = {Sankaku Tags Categorize for WD14Tagger}, author = {deepghs}, howpublished = {\url{https://huggingface.co/datasets/deepghs/sankaku_tags_categorize_for_WD14Tagger}}, year = {2023}, note = {Comprehensive tag categorization dataset from Sankaku Complex for enhancing WD14Tagger image annotation capabilities}, abstract = {This dataset provides a comprehensive tag categorization system specifically designed for enhancing the WD14Tagger model with Sankaku Complex metadata. The dataset contains 10,861 meticulously classified tags from the Sankaku Complex platform, organized into distinct categories to improve tag-based image classification and generation tasks. Each tag entry includes the original Sankaku tag ID, tag name, assigned category, and usage count, enabling fine-grained control over tag processing in image recognition pipelines.}, keywords = {tag-categorization, image-annotation, metadata-enhancement, WD14Tagger, Sankaku-Complex} } ```
# 适用于WD14Tagger的Sankaku Complex标签分类数据集 ## 摘要 本数据集提供了一套完整的**标签分类系统**，专为借助Sankaku Complex元数据优化WD14Tagger模型而设计。数据集包含来自Sankaku Complex平台的**10861条经过精细分类的标签**，按不同类别进行组织，以优化基于标签的图像分类与生成任务。每条标签条目均包含原始Sankaku标签ID、标签名称、分配的类别以及使用次数，可实现图像识别流程中对标签处理的精细化控制。 该**分类组织方式**采用结构化方法，将标签划分为符合通用图像标注需求的语义类别。此分类系统可在自动化图像打标系统中实现更智能的标签筛选、优先级排序与处理流程。本数据集充当了Sankaku Complex庞大标签生态与WD14Tagger框架之间的**桥梁**，提供标准化的类别映射关系，从而提升模型性能与可解释性。 本数据集包含带有使用频率统计信息的**详细元数据**，可支持图像打标模型的训练与推理全流程。类别分配可基于语义相关性实现选择性标签处理，减少噪声并提升生成标签的质量。这对于从事二次元风格美术作品创作的**内容创作者与研究者**尤为重要，他们需要对图像数据集进行精准且有序的标签管理。 本数据集的结构可实现与现有WD14Tagger工作流的**无缝集成**，同时引入Sankaku Complex丰富的标签分类体系。通过提供类别级别的组织方式与单条标签数据，该数据集可在计算机视觉领域实现更复杂的基于标签的图像检索、分类与生成应用。关键词：**标签分类**、**图像标注**、**元数据增强**、**WD14Tagger**、**Sankaku Complex** ## 数据集结构 本数据集仅包含一个CSV文件，其字段规范如下： - `tag_id`：来自Sankaku Complex的唯一标识符 - `name`：实际的标签文本/名称 - `category`：数值化的类别分配（如0、3等） - `count`：该标签在Sankaku Complex上的使用频率统计 ### 示例数据 | tag_id | name | category | count | |--------|------|----------|-------| | 478554 | contrast | 0 | 850 | | 2075818 | nike_(company) | 3 | 1884 | | 2068712 | hugging_book | 3 | 870 | | 2035712 | rhodes_island_logo_(arknights) | 3 | 976 | | 2006775 | unworn_goggles | 3 | 699 | ## 使用方法 python import pandas as pd # 加载数据集 df = pd.read_csv('selected_tags_classified.csv') # 探索数据 print(f"总标签数：{len(df)}") print(f"类别：{df['category'].unique()}") print(f"类别0的示例标签：") print(df[df['category'] == 0].head()) # 获取最常用的标签 top_tags = df.sort_values('count', ascending=False).head(10) print("最热门的标签：") print(top_tags[['name', 'category', 'count']]) ## 与WD14Tagger的集成 本数据集可通过提供基于类别的标签筛选与组织方式，优化WD14Tagger的功能： python # 示例：按类别筛选标签以满足特定用例 def get_tags_by_category(df, category_ids): return df[df['category'].isin(category_ids)] # 获取特定类别的所有标签 character_tags = get_tags_by_category(df, [3]) # 示例：代表角色/对象标签的类别3 ## 原始内容说明 本数据集包含来自Sankaku Complex的10861条分类标签，具备以下特征： - 总条目数：10861 - 文件大小：326595字节 - 字段：tag_id、name、category、count - 涵盖类别：多个数值化类别（如0、3等） ## 引用格式 bibtex @misc{sankaku_tags_categorize_wd14tagger, title = {Sankaku Tags Categorize for WD14Tagger}, author = {deepghs}, howpublished = {url{https://huggingface.co/datasets/deepghs/sankaku_tags_categorize_for_WD14Tagger}}, year = {2023}, note = {本数据集为来自Sankaku Complex的完整标签分类数据集，用于优化WD14Tagger的图像标注能力}, abstract = {本数据集提供了一套完整的标签分类系统，专为借助Sankaku Complex元数据优化WD14Tagger模型而设计。本数据集包含来自Sankaku Complex平台的10861条经过精细分类的标签，按不同类别进行组织，以优化基于标签的图像分类与生成任务。每条标签条目均包含原始Sankaku标签ID、标签名称、分配的类别以及使用次数，可实现图像识别流程中对标签处理的精细化控制。}, keywords = {tag-categorization, image-annotation, metadata-enhancement, WD14Tagger, Sankaku-Complex} }
