datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
rocketleague-analysis
Rocket League Analysis
Local Rocket League replay analysis using Ballchasing API exports and plain DuckDB.
The report is meant to answer one practical question: what should I work on next from my saved replay sample?
Quick Start
mise install
mise run setup
mise run test
mise exec -- python scripts/analyze_scenarios.py \
--replay-dir /path/to/Rocket\ League/TAGame/Demos \
--limit 10
Start with CONTRIBUTING.md before changing the pipeline.
Replay files and… See the full description on the dataset page: https://huggingface.co/datasets/edmundmiller/rocketleague-analysis.retina-age-analysis
Retina Age Analysis Dataset
Dataset Description
This dataset contains 9,857 retinal fundus images from 5,393 patients for age prediction tasks.
Dataset Summary
Task: Age prediction from retinal fundus images
Images: 9,857 high-quality retinal images
Patients: 5,393 unique patients
Age Range: 5-97 years
Image Format: JPEG
Average Image Size: ~1 MB
Supported Tasks
Regression: Predict continuous age (5-97 years)
Classification: Predict age group (5… See the full description on the dataset page: https://huggingface.co/datasets/ramankamran/retina-age-analysis.risk-analysisstartup-Investments-analysis
📊 StartUp Investments EDA
1. Background & Objectives
This project explores a comprehensive dataset of startup investments (sourced from Crunchbase) to uncover the primary factors that predict a startup's survival and trajectory in a competitive market.
Through this Exploratory Data Analysis (EDA), we analyze historical funding data, investment rounds, and market categories to determine which variables drive specific company outcomes - namely, whether a business… See the full description on the dataset page: https://huggingface.co/datasets/lia-prop13/startup-Investments-analysis.Deforest-Analysis
NRT Forest-Loss Test Set for Student Analysis
This package contains the fixed held-out test split used for a study of
near-real-time forest-loss detection from four HLS observations. It is an
analysis release: it includes inputs, labels, model outputs, and visual
renders, but no checkpoints or GPU-dependent code.
The intended analyses are prediction-shape comparison, per-connected-component
performance, and seasonal performance. Do not use this test set to select model… See the full description on the dataset page: https://huggingface.co/datasets/mqraitem/Deforest-Analysis.