datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mlcd-mteb-cifar-eval
MLCD vs CLIP on MTEB CIFAR-10/100: integration and evaluation
Evaluation results accompanying the MTEB integration of two MLCD image encoders
(PR #5406, resolving
issue #2571).
Two DeepGlint-AI MLCD encoders were integrated into MTEB, verified against the
reference implementation, and evaluated on the official MTEB CIFAR-10/CIFAR-100
image-classification tasks alongside size-matched OpenAI CLIP baselines.
What was measured
Official MTEB image classification: 5… See the full description on the dataset page: https://huggingface.co/datasets/b4ph/mlcd-mteb-cifar-eval.svae-freckles-4096-cifar10
SVAE Freckles 4096 — CIFAR-10 Omega Tokens
Precomputed spectral decomposition of CIFAR-10 through Freckles v41 (256×256), a frozen Spectral Variational Autoencoder trained exclusively on synthetic noise.
Each CIFAR-10 image is resized to 256×256, decomposed into 4096 patches (4×4 each), and passed through Freckles' encoder → SVD bottleneck. The 4 singular values per patch are stored as a (4, 64, 64) omega map — a 4-channel spatial representation of spectral energy.… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/svae-freckles-4096-cifar10.cifar10-stats
CIFAR-10 CNN Layerwise Training Statistics
Dataset Description
This dataset contains layer-wise training statistics for a convolutional network trained on CIFAR-10, together with the corresponding test/acc.
Each row is one point at loss landscape. The features are computed on the last training batch of 1024 samples before the end of an epoch, and test/acc is measured immediately after that epoch.
The dataset includes statistics for several convolutional layers and the… See the full description on the dataset page: https://huggingface.co/datasets/Fullfix/cifar10-stats.