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  This is the dataset proposed in our paper [**Image Copy Detection for Diffusion Models**](https://arxiv.org/abs/2410.xxxxx) (NeurIPS 2024).
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  D-Rep consists of 40, 000 image-replica pairs, in which each replica is generated by a diffusion model. The 40, 000 image-replica pairs are manually labeled with 6 replication levels ranging from 0 (no replication) to 5 (total replication). We divide D-Rep into a training set with 90% (36, 000) pairs and a test set with the remaining 10% (4, 000) pairs.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  This is the dataset proposed in our paper [**Image Copy Detection for Diffusion Models**](https://arxiv.org/abs/2410.xxxxx) (NeurIPS 2024).
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  D-Rep consists of 40, 000 image-replica pairs, in which each replica is generated by a diffusion model. The 40, 000 image-replica pairs are manually labeled with 6 replication levels ranging from 0 (no replication) to 5 (total replication). We divide D-Rep into a training set with 90% (36, 000) pairs and a test set with the remaining 10% (4, 000) pairs.
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+
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+ # Download
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+
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+ ### Automatical
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+ Install the [datasets](https://huggingface.co/docs/datasets/en/installation) library first, by:
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+ ```
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+ pip install datasets
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+ ```
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+ Then it can be downloaded automatically with
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+ ```python
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+ import numpy as np
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+ from datasets import load_dataset
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+ dataset = load_dataset('WenhaoWang/D-Rep')
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+ ```