Update README.md

README.md

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 - VLMs
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-# Dataset Card for Dataset Name
+# Dataset Card for ImageNet_10k Dataset
 
-<!-- Provide a quick summary of the dataset. -->
-
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+This dataset is derived from ImageNet and contains 10,000 image-label pairs, designed for binary classification in object detection tasks.
 
 ## Dataset Details
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
-
-
+This dataset consists of 10,000 image-label pairs sampled from ImageNet. 5,000 pairs have correct image-label matches (positive examples labeled "yes"), and 5,000 pairs have random labels assigned from the ImageNet 1000-class taxonomy (negative examples labeled "no"). The dataset is intended for training and evaluating object detection systems with a binary classification component.
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
+- **Language(s) (NLP):** English (for labels)
+- **License:** The dataset inherits the ImageNet license terms (Custom license - requires acceptance of Terms of Access)
 
-### Dataset Sources [optional]
+### Dataset Sources
 
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** Based on ImageNet (https://image-net.org/)
+- **Paper:** Based on ImageNet (Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. CVPR 2009.)
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
-
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
+This dataset is suitable for:
+- Training binary object detection classifiers
+- Evaluating object recognition systems' accuracy
+- Testing models' ability to verify if an image contains a specified object
+- Benchmarking computer vision systems on object verification tasks
 
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+The dataset consists of 10,000 examples with the following fields:
+- `idx`: int64 - A unique identifier for each example
+- `image`: image - The image data from ImageNet
+- `label`: string - The object label being verified (from ImageNet's 1000 classes)
+- `gt_ans`: string - Ground truth answer ("yes" for correct label, "no" for random incorrect label)
 
-[More Information Needed]
+The dataset is evenly split between positive examples (5,000 images with their correct ImageNet labels, marked "yes") and negative examples (5,000 images with randomly assigned incorrect labels from ImageNet's taxonomy, marked "no").
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+This dataset was created to provide a balanced binary classification task for object detection systems. By including both correct and incorrect image-label pairs, it supports the development of models that can verify whether a specific object appears in an image.
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
+- 5,000 images were randomly sampled from ImageNet along with their correct labels
+- 5,000 additional images were sampled and assigned random labels from the ImageNet 1000-class taxonomy
+- All correct label pairs were marked with "yes" in the gt_ans field
+- All random label pairs were marked with "no" in the gt_ans field
+- The complete set of 10,000 examples was assigned unique indices
 
 #### Who are the source data producers?
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
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-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-
-#### Annotation process
-
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
-
-#### Who are the annotators?
+The original images come from ImageNet, which collected images from the web. The labels were originally created by ImageNet annotators through a combination of automated and manual processes. The binary classification labels ("yes"/"no") were added during the creation of this derivative dataset.
 
-<!-- This section describes the people or systems who created the annotations. -->
+### Personal and Sensitive Information
 
-[More Information Needed]
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+This dataset inherits the privacy considerations of ImageNet. While efforts were made in ImageNet to remove certain personally identifiable information, users should be aware that the images may contain people, locations, or other potentially identifying information. No additional personal data was introduced during the creation of this derivative dataset.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+Users should:
+- Be aware of ImageNet's documented biases when using this dataset
+- Evaluate model performance across different object categories to identify potential performance disparities
+- Consider augmenting with more diverse data sources for production applications
+- Use this as a benchmark or starting point rather than a complete solution for production object detection
 
-**BibTeX:**
-
-[More Information Needed]
 
 **APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
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-## Dataset Card Authors [optional]
-
-[More Information Needed]
+ImageNet Object Detection Dataset. (2025). Derived from ImageNet by Deng et al., 2009.
 
 ## Dataset Card Contact
 
-[More Information Needed]
+[Your contact information here]