Updated Community Forensics Eval set descriptions.

README.md

@@ -84,7 +84,7 @@ Our dataset is formatted in a Parquet data frame of the following structure:
 `Systematic` (1,919,493 images): Systematically downloaded subset of the data (data downloaded from Hugging Face via automatic pipeline) \
 `Manual` (774,023 images): Manually downloaded subset of the data \
 `Commercial` (14,918 images): Commercial models subset \
-`PublicEval` (51,836 images): Evaluation set where generated images are paired with COCO or FFHQ for license-compliant redistribution. Note that these are not the "source" datasets used to sample the generated images
+`PublicEval` (51,836 images): Evaluation set where generated images are paired with COCO or FFHQ for license-compliant redistribution. Note that these are not the "source" datasets used to sample the generated images (now superseded by [CommunityForensics-Eval](https://huggingface.co/datasets/OwensLab/CommunityForensics-Eval) evaluation set, which are paired with accurate source datasets.)
 
 ## Usage examples
 
@@ -96,7 +96,8 @@ import io
 
 # default training set
 commfor_train = ds.load_dataset("OwensLab/CommunityForensics", split="Systematic+Manual", cache_dir="~/.cache/huggingface/datasets")
-commfor_eval = ds.load_dataset("OwensLab/CommunityForensics", split="PublicEval", cache_dir="~/.cache/huggingface/datasets")
+commfor_eval = ds.load_dataset("OwensLab/CommunityForensics-Eval", split="CompEval", cache_dir="~/.cache/huggingface/datasets")
+
 
 # optionally shuffle the dataset
 commfor_train = commfor_train.shuffle(seed=123, writer_batch_size=3000)
@@ -123,7 +124,7 @@ import io
 commfor_sys_stream = ds.load_dataset("OwensLab/CommunityForensics", split='Systematic', streaming=True)
 
 # streaming only the evaluation set
-commfor_eval_stream = ds.load_dataset("OwensLab/CommunityForensics", split='PublicEval', streaming=True)
+commfor_eval_stream = ds.load_dataset("OwensLab/CommunityForensics-Eval", split='CompEval', streaming=True)
 
 # optionally shuffle the streaming dataset
 commfor_sys_stream = commfor_sys_stream.shuffle(seed=123, buffer_size=3000)
@@ -146,6 +147,8 @@ To accurately reproduce our training settings, it is necessary to download all d
 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.
 
+For evaluation, please check our [Community Forensics-Eval](https://huggingface.co/datasets/OwensLab/CommunityForensics-Eval) set.
+
 ### Real data composition for training
 When training our [classifiers](https://huggingface.co/OwensLab/commfor-model-384), we used the following real data composition:
 ```