Update README.md

README.md

@@ -139,7 +139,7 @@ Please check [Hugging Face documentation](https://huggingface.co/docs/datasets/v
 
 ### Training fake image classifiers
 For training a fake image classifier, it is necessary to pair the generated images with "real" images (here, "real" refers to images that are not generated by AI).
-In our [paper](https://arxiv.org/abs/2411.04125), we used 11 different image datasets: [LAION](https://laion.ai/)([our training distribution](https://huggingface.co/datasets/OwensLab/CommunityForensics/blob/main/data/Real/laion_commfor_train_subset_2M.csv)/[test data](https://huggingface.co/datasets/OwensLab/CommunityForensics/blob/main/data/Real/laion_commfor_test_subset_10K.csv)), [ImageNet](https://www.image-net.org/), [COCO](https://cocodataset.org/), [FFHQ](https://github.com/NVlabs/ffhq-dataset), [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html), [MetFaces](https://github.com/NVlabs/metfaces-dataset), [AFHQ-v2](https://github.com/clovaai/stargan-v2/), [Forchheim](https://faui1-files.cs.fau.de/public/mmsec/datasets/fodb/), [IMD2020](https://staff.utia.cas.cz/novozada/db/), [Landscapes HQ](https://github.com/universome/alis), and [VISION](https://lesc.dinfo.unifi.it/VISION/), for sampling the generators and training the [classifiers](https://huggingface.co/OwensLab/commfor-model-384).
+In our [paper](https://arxiv.org/abs/2411.04125), we used 11 different image datasets: [LAION](https://laion.ai/)([our training distribution](https://huggingface.co/datasets/OwensLab/CommunityForensics/blob/main/data/Real/laion_commfor_train_subset_2M.csv)), [ImageNet](https://www.image-net.org/), [COCO](https://cocodataset.org/), [FFHQ](https://github.com/NVlabs/ffhq-dataset), [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html), [MetFaces](https://github.com/NVlabs/metfaces-dataset), [AFHQ-v2](https://github.com/clovaai/stargan-v2/), [Forchheim](https://faui1-files.cs.fau.de/public/mmsec/datasets/fodb/), [IMD2020](https://staff.utia.cas.cz/novozada/db/), [Landscapes HQ](https://github.com/universome/alis), and [VISION](https://lesc.dinfo.unifi.it/VISION/), for sampling the generators and training the [classifiers](https://huggingface.co/OwensLab/commfor-model-384).
 To accurately reproduce our training settings, it is necessary to download all datasets and pair them with the generated images.
 We understand that this may be inconvenient for simple prototyping, 
 and thus we also release [Community Forensics-Small](https://huggingface.co/datasets/OwensLab/CommunityForensics-Small) dataset, which is paired with real datasets that have redistributable licenses and contains roughly 11% of the base dataset.
@@ -159,7 +159,7 @@ VISION          0.25 %
 Forchheim       0.13 %
 Metfaces        0.05 %
 ```
-We clipped the `LAION` and `ImageNet` data to around 1.08M images to ensure that the ratio of real/fake is 1:1. We release the links to the LAION data subset we used here: [train](https://huggingface.co/datasets/OwensLab/CommunityForensics/blob/main/data/Real/laion_commfor_train_subset_2M.csv)/[test](https://huggingface.co/datasets/OwensLab/CommunityForensics/blob/main/data/Real/laion_commfor_test_subset_10K.csv). Please note that the data may not be exactly reproducible due to link rot.
+We clipped the `LAION` and `ImageNet` data to around 1.08M images to ensure that the ratio of real/fake is 1:1. We release the links to the LAION data subset we used for training [here](https://huggingface.co/datasets/OwensLab/CommunityForensics/blob/main/data/Real/laion_commfor_train_subset_2M.csv). Please note that the data may not be exactly reproducible due to link rot.
 
 # Dataset Creation
 ## Curation Rationale