{"url":"/dataset/kid-f","name":"KID-F","full_name":"K-pop Idol Dataset - Female","description_markdown":"# Description\r\nK-pop Idol Dataset - Female (KID-F) is the first dataset of K-pop idol high quality face images. It consists of about 6,000 high quality face images at 512x512 resolution and identity labels for each image.\r\n\r\nWe collected about 90,000 K-pop female idol images and crop the face from each image. And we classified high quality face images. As a result, there are about 6,000 high quality face images in this dataset.\r\n\r\nThere are 300 test datasets for a benchmark. There are no duplicate images between test and train images. Some identities in test images are not duplicated with train images. (It means some test images is new identity to the trained model) Each test images have its degraded pair. You can use these degraded test images for testing face super resolution performance.\r\n\r\nWe also provide identity labels for each image. You can download the csv file from our [github](https://github.com/PCEO-AI-CLUB/KID-F)\r\n\r\n# Download\r\nYou can download dataset from here.\r\n[Google Drive](https://drive.google.com/drive/folders/15RbdHeLymfKA_Xm96rIrGe4Dt5iCQ75E?usp=sharing)\r\n\r\n# Agreement\r\n- The use of this software is RESTRICTED to **non-commercial** research and educational purposes.\r\n- All images of the KID-F dataset are obtained from the internet which are not property of EDA(PCEO-AI-CLUB). EDA is not responsible for the content nor the meaning of these images.\r\n- You agree **not to** reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.\r\n- You agree **not to** further copy, publish or distribute any portion of the KID-F dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.\r\n- EDA reserves the right to terminate your access to the CelebA dataset at any time.","description_withheld":null,"homepage":"https://github.com/PCEO-AI-CLUB/KID-F","introduced_date":"2022-07-28","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Super-Resolution","url":"/task/super-resolution","datasets_with_task":"/datasets/task/super-resolution"},{"name":"Blind Super-Resolution","url":"/task/blind-super-resolution","datasets_with_task":"/datasets/task/blind-super-resolution"},{"name":"Face Hallucination","url":"/task/face-hallucination","datasets_with_task":"/datasets/task/face-hallucination"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["KID-F"],"data_loaders":[],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}