VilaVision/Imageclassifier

This dataset consists of **994 images** generated using **DALL·E** and **Midjourney**. Each image is annotated with detailed textual descriptions and the count of distinct objects present, using Nvidia's Nim VLM API. The dataset is designed for use in image captioning, text-to-image generation, and image segmentation tasks. ## Dataset Details ### Dataset Description This dataset includes images generated by AI models, specifically DALL·E and Midjourney. Each image is annotated with: - A complete textual description of the scene. - The number of distinct objects present in the image. The images cover a diverse range of scenes and objects, providing a valuable resource for developing and evaluating models in various computer vision and natural language processing tasks. - **Curated by:** Alok Pandey - **License:** Apache-2.0 ### Dataset Sources - **Repository:** https://huggingface.co/datasets/alokpandey/Image_classifier - **Generated by:** DALL·E and Midjourney - **Annotated using:** Nvidia Nim VLM API ## Uses ### Direct Use This dataset is suitable for: - **Image Captioning**: Training models to generate detailed descriptions based on AI-generated images. - **Object Detection**: Developing and evaluating models for detecting and counting objects within images. - **Text-to-Image Generation**: Enhancing models that generate images from textual descriptions. ### Out-of-Scope Use The dataset is not intended for: - **Real-world Data Validation**: Since images are generated by AI, they may not accurately represent real-world objects or scenes. - **Personal or Sensitive Data Analysis**: The dataset does not contain personal or sensitive information, but care should be taken when using it in broader applications. ## Dataset Structure The dataset is organized as follows: ``` /dataset-directory/ │ ├── images/ │ ├── image_1.jpg │ ├── image_2.jpg │ ├── ... │ ├── annotations/ │ ├── descriptions.csv │ ├── object_counts.csv ``` - **images/**: Contains all the AI-generated image files. - **annotations/** - **descriptions.csv**: Contains image filenames and corresponding descriptions. - **object_counts.csv**: Contains image filenames and the count of objects. ## Dataset Creation ### Curation Rationale The dataset was created to provide high-quality, diverse image descriptions and object counts generated by state-of-the-art AI models. It supports various computer vision and natural language processing tasks by offering well-annotated data. ### Source Data #### Data Collection and Processing Images were generated using DALL·E and Midjourney, advanced AI models capable of creating detailed and diverse visuals from textual prompts. Each image was annotated using Nvidia's Nim VLM API, which provided textual descriptions and object counts based on the generated images. #### Who are the source data producers? The images were produced by DALL·E and Midjourney, AI models developed by [OpenAI and Midjourney, respectively]. Annotations were generated using Nvidia's Nim VLM API. ### Annotations #### Annotation Process Images were annotated with descriptions and object counts using Nvidia's Nim VLM API. The API provided natural language descriptions and object counts based on the visual content of the images. #### Who are the annotators? Annotations were performed using Nvidia's Nim VLM API, a tool designed for high-quality image annotation and description generation. #### Personal and Sensitive Information The dataset does not contain personal or sensitive information, as all images are AI-generated and annotations are based on these images. ## Bias, Risks, and Limitations ### Recommendations Users should be aware that the images are generated by AI models and may not represent real-world scenarios accurately. The dataset should be used with caution and supplemented with real-world data for more comprehensive model training. ## Citation **BibTeX:** ```bibtex @dataset{your_name_image_description_2024, author = Alok Pandey, title = Image_classifier, year = {2024}, license = {Apache-2.0}, } ``` **APA:** Alok Pandey. (2024). *Image_classifier*.https://huggingface.co/datasets/alokpandey/Image_classifier ## Dataset Card Contact For inquiries, contact: - **Name**: Alok Pandey - **Email**: [e-mail]本数据集包含**994张**由**DALL·E**与**Midjourney**生成的图像。每张图像均通过英伟达Nim VLM API（Nvidia Nim VLM API）标注了详细的文本描述与图像内不同物体的数量。本数据集适用于图像字幕生成、文本到图像生成以及图像分割任务。 ## 数据集详情 ### 数据集描述 本数据集包含由人工智能模型生成的图像，具体为DALL·E与Midjourney。每张图像均标注了以下内容： - 场景的完整文本描述。 - 图像内存在的不同物体的数量。 该数据集涵盖多样化的场景与物体类型，可为各类计算机视觉与自然语言处理任务中的模型开发与评估提供宝贵的资源。 - **整理者：** 阿洛克·潘德（Alok Pandey） - **授权协议：** Apache-2.0 ### 数据集来源 - **仓库地址：** https://huggingface.co/datasets/alokpandey/Image_classifier - **生成方：** DALL·E与Midjourney - **标注工具：** 英伟达Nim VLM API（Nvidia Nim VLM API） ## 使用场景 ### 直接使用场景 本数据集适用于： - **图像字幕生成：** 训练基于人工智能生成图像生成详细描述的模型。 - **物体检测：** 开发并评估用于检测图像内物体并统计其数量的模型。 - **文本到图像生成：** 优化基于文本描述生成图像的模型。 ### 非适用场景 本数据集不适用于： - **真实世界数据验证：** 由于图像由人工智能生成，其可能无法准确还原真实世界的物体与场景。 - **个人或敏感数据分析：** 尽管本数据集未包含个人或敏感信息，但在将其应用于更广泛的场景时仍需谨慎。 ## 数据集结构 本数据集的组织形式如下： /dataset-directory/ │ ├── images/ │ ├── image_1.jpg │ ├── image_2.jpg │ ├── ... │ ├── annotations/ │ ├── descriptions.csv │ ├── object_counts.csv - **images/：** 存储所有人工智能生成的图像文件。 - **annotations/：** - **descriptions.csv：** 存储图像文件名与对应的文本描述。 - **object_counts.csv：** 存储图像文件名与物体数量统计结果。 ## 数据集构建 ### 构建初衷 本数据集旨在提供由前沿人工智能模型生成的高质量、多样化的图像描述与物体数量统计结果，通过提供高质量标注数据，为各类计算机视觉与自然语言处理任务提供支撑。 ### 源数据 #### 数据收集与处理 图像由DALL·E与Midjourney生成，这两款先进的人工智能模型可基于文本提示生成细节丰富、类型多样的视觉内容。每张图像均通过英伟达Nim VLM API进行标注，该API可基于图像视觉内容生成文本描述与物体数量统计结果。 #### 源数据生产者是谁？ 图像由DALL·E与Midjourney生成，二者分别为OpenAI与Midjourney开发的人工智能模型。标注结果通过英伟达Nim VLM API生成。 ### 标注信息 #### 标注流程 图像通过英伟达Nim VLM API完成描述与物体数量标注，该API可基于图像视觉内容生成自然语言描述与物体数量统计结果。 #### 标注执行者是谁？ 标注工作通过英伟达Nim VLM API完成，该工具专为高质量图像标注与描述生成而设计。 #### 个人与敏感信息 本数据集未包含任何个人或敏感信息，因所有图像均由人工智能生成，且标注结果均基于此类图像。 ## 偏差、风险与局限性 ### 使用建议 用户需注意，本数据集内的图像均由人工智能模型生成，可能无法准确还原真实世界场景。在进行更全面的模型训练时，应谨慎使用本数据集，并补充真实世界数据。 ## 引用格式 **BibTeX：** bibtex @dataset{your_name_image_description_2024, author = Alok Pandey, title = Image_classifier, year = {2024}, license = {Apache-2.0}, } **APA格式：** 阿洛克·潘德（Alok Pandey）. (2024). *Image_classifier*.https://huggingface.co/datasets/alokpandey/Image_classifier ## 数据集卡片联系方式 如有疑问，请联系： - **姓名：** 阿洛克·潘德（Alok Pandey） - **邮箱：** [e-mail]