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--- |
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language: |
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- en |
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license: mit |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- text-to-image |
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- image-feature-extraction |
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tags: |
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- diffusion models |
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- image copy detection |
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dataset_info: |
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features: |
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- name: Name |
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dtype: string |
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- name: Level |
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dtype: int64 |
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- name: generated_images |
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dtype: image |
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- name: real_images |
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dtype: image |
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splits: |
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- name: Test |
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num_bytes: 2538590040.0 |
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num_examples: 4000 |
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- name: Train |
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num_bytes: 22265208436.0 |
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num_examples: 36000 |
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download_size: 24773596239 |
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dataset_size: 24803798476.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: Test |
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path: data/Test-* |
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- split: Train |
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path: data/Train-* |
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--- |
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|
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<p align="center"> |
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<img src="https://huggingface.co/datasets/WenhaoWang/D-Rep/resolve/main/D-Rep.png" width="800"> |
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</p> |
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|
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# Summary |
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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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# Download |
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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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``` |
