datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
features-dinov3-vith16plus-224-imagenet-22k-wdsimagenet-clip-features-orderlyimagenet-clip-features
Update: 10/2/2025
Claude said that I'm not being careful enough with my database curation after grilling me for 20 minutes, so I included the preparer script as well.
Claude Sonnet 4.5 is kind of a chad.
Update; 9/26/2025
Having to download this whole repo is annoying, so I'm making sure the splits are named train/val/test (if they exist) and the named subset is the clip name.
Older non-dated updates
Everything extracted with torch configured as deterministic;… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/imagenet-clip-features.imagenet-64-ffhq-features
Inception-V3 Feature Vectors for FFHQ and ImageNet
This repository contains pre-computed Inception-V3 feature vectors for the FFHQ and ImageNet datasets, released to support reproducibility of results in Adaptive Second-Order Solvers for Generative Diffusion Sampling. Computing these features from scratch takes approximately one day; this release lets others verify or build on our results without repeating that cost.
This dataset is for non-commercial academic/research use… See the full description on the dataset page: https://huggingface.co/datasets/ellakemperman/imagenet-64-ffhq-features.imagenet_features_1024_sd_vae_ft_emasd_vae_features_imagenet_1k_256x256imagenet-backbone-features-fp32
ImageNet backbone features (fp32)
Activations pré-extraites d'ImageNet-1k pour trois backbones, mémoire-mappables,
utilisées pour entraîner les SAE du repo SAE_CBM_unification.
Contenu
136 shards .npy, 37.2 Go, fp32.
Backbone
Node
Split
Shards
Dim
Taille
resnet50
avgpool
train / val
14 / 2
2048
9.8 Go / 392 Mo
resnet50
layer1
train / val
16 / 4
—
1.3 Go / 51 Mo
resnet50
layer2
train / val
16 / 4
—
2.5 Go / 100 Mo
resnet50
layer3
train / val
16 / 4… See the full description on the dataset page: https://huggingface.co/datasets/AlexandreRocchi/imagenet-backbone-features-fp32.