Image Feature Extraction Datasets

Dataset · Image feature extraction

FOMO260K

UCPH DIKU Foundation Models for Brain MRI

FOMO260K: Brain MRI Dataset for Large-Scale Self-Supervised Learning with Clinical Data Dataset paper preprint: A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning. https://arxiv.org/pdf/2506.14432. Description FOMO260K is a large-scale dataset of brain MRI scans, including both clinical and research-grade scans. The dataset includes a wide range of sequences, including T1, MPRAGE, T2, T2, FLAIR, SWI, T1c, PD, DWI

Co-registered Sentinel-1 / Sentinel-2 pairs for self-supervised pre-training in Earth observation. Generated 2026-08-09. Every entry is a pair: one Sentinel-2 optical tile and one Sentinel-1 radar tile over the same ground, written to the same grid — identical bounding box, identical CRS (the tile's local UTM zone) and 10 m pixels — so the two rasters correspond pixel for pixel with no resampling on your side. The two acquisitions are at most three days apart; the realised gap is recorded per pair. Each tile is 512 × 512 pixels at 10 m, so it covers 5.12 × 5.12 km. The remaining Sentinel bands are not shipped, to keep the corpus trainable on ordinary hardware. They remain recoverable: every…