datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
imagenette-320px-resplit
Imagenette 320px with Fixed Validation and Test Splits
Dataset Description
This dataset is a reproducible, Parquet-based version of the 320px configuration of frgfm/imagenette. Imagenette is a subset of ten readily classified ImageNet classes created for fast experimentation with image-classification methods.
This version preserves the source images, numeric labels, and label metadata. Its only data change is a fixed, stratified division of the original validation… See the full description on the dataset page: https://huggingface.co/datasets/leandrodevai/imagenette-320px-resplit.imagewoof-320px-resplit
ImageWoof 320px with Fixed Validation and Test Splits
Dataset Description
This dataset is a reproducible, Parquet-based version of the 320px configuration of frgfm/imagewoof. ImageWoof is a subset of ten dog-breed classes from ImageNet designed to be more difficult than broad-category image-classification benchmarks.
This version is intended for image classification and confidence-calibration experiments. It introduces two changes to the source dataset:
It… See the full description on the dataset page: https://huggingface.co/datasets/leandrodevai/imagewoof-320px-resplit.Deepfakes-QA-Leaning
Deepfake Quality Assessment
Deepfake QA is a Deepfake Quality Assessment model designed to analyze the quality of deepfake images & videos. It evaluates whether a deepfake is of good or bad quality, where:
0 represents a bad-quality deepfake
1 represents a good-quality deepfake
This classification serves as the foundation for training models on deepfake quality assessment, helping improve deepfake detection and enhancement techniques.
Citation… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Deepfakes-QA-Leaning.Deepfakes-QA-Leaning
Deepfake Quality Assessment
Deepfake QA is a Deepfake Quality Assessment model designed to analyze the quality of deepfake images & videos. It evaluates whether a deepfake is of good or bad quality, where:
0 represents a bad-quality deepfake
1 represents a good-quality deepfake
This classification serves as the foundation for training models on deepfake quality assessment, helping improve deepfake detection and enhancement techniques.
Citation… See the full description on the dataset page: https://huggingface.co/datasets/strangerguardhf/Deepfakes-QA-Leaning.