aha
Datasets
All datasets matching “aha”pointodysseyFeb 21: updated to v1.2. Please see https://github.com/y-zheng18/point_odyssey/tree/main for release notes.
aharensanwahakarenaiseason2
Bangumi Image Base of Aharen-san Wa Hakarenai Season 2
This is the image base of bangumi Aharen-san wa Hakarenai Season 2, we detected 65 characters, 6341 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/aharensanwahakarenaiseason2.csvfiverr-gigs
Fiverr Gigs — community dataset
Public Fiverr listing metadata collected via the
fiverr-gig-optimizer
Claude Code skill. Grown via opt-in contributions from users who run
contribute.py. Licensed CC-BY-4.0.
What's in it
Each record follows the canonical schema (title, category/subcategory, tier
prices, delivery days, tags, rating, review_count, gig_count_in_search,
currency). Prices are normalized to USD.
Privacy
Contributions are anonymized before… See the full description on the dataset page: https://huggingface.co/datasets/Ahad690/fiverr-gigs.growthkit-trends
GrowthKit Trends
A community, opt-in, federated dataset of public, anonymized short-form
short-form-video trend and benchmark observations, contributed by users of the
open-source GrowthKit Claude
Code skill. It improves GrowthKit's default benchmarks over time so every
founder starts from better, source-tagged ranges instead of fabricated numbers.
Honesty first. GrowthKit never lets a model invent a market metric. Numbers
come from deterministic scripts run on a founder's own… See the full description on the dataset page: https://huggingface.co/datasets/Ahad690/growthkit-trends.rvl_cdipThe RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images.