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  # Dataset Card for Dataset Name
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- ## Dataset Description
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- - **Homepage:**
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- - **Repository:**
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- - **Paper:**
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- - **Leaderboard:**
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- - **Point of Contact:**
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  ### Dataset Summary
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- 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).
 
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- ### Supported Tasks and Leaderboards
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- [More Information Needed]
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  ### Languages
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- [More Information Needed]
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- ## Dataset Structure
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- ### Data Instances
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- ### Data Fields
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- ### Data Splits
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- ## Dataset Creation
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- ### Curation Rationale
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- [More Information Needed]
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  ### Source Data
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  https://www.kaggle.com/competitions/landmark-recognition-2021/data
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- #### Initial Data Collection and Normalization
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- [More Information Needed]
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- #### Who are the source language producers?
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- ### Annotations
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- #### Annotation process
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- #### Who are the annotators?
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- ### Personal and Sensitive Information
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- ## Considerations for Using the Data
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- ### Social Impact of Dataset
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- ### Discussion of Biases
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- ### Other Known Limitations
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- ## Additional Information
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- ### Dataset Curators
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- ### Licensing Information
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  ### Citation Information
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- ### Contributions
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- [More Information Needed]
 
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  # Dataset Card for Dataset Name
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  ### Dataset Summary
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+ This dataset is a subset of Kaggle's Google Landmark Recognition 2021 competition with only the categories with more than 500 images.
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+ https://www.kaggle.com/competitions/landmark-recognition-2021/data
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+ The dataset consists of a total of 45579 224x224 color images in 51 classes.
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+ The dataset was randomly split with 80% of the images for the train set and 20% for the test set.
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  ### Languages
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+ English
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Source Data
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  https://www.kaggle.com/competitions/landmark-recognition-2021/data
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  ### Citation Information
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+ "Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval", T. Weyand, A. Araujo, B. Cao and J. Sim, Proc. CVPR'20
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+ "Towards A Fairer Landmark Recognition Dataset", Z. Kim, A. Araujo, B. Cao, C. Askew, J. Sim, M. Green, N. Yilla and T. Weyand, arxiv:2108.08874
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