Datasets:
ImageNet terms of access
This dataset contains processed ImageNet images. Read the ImageNet Terms of Access at https://image-net.org/download.php before requesting access. Access is automatically approved after acceptance.
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Priming: packed ImageNet-256 training data
The exact packed image dataset used by Priming, with 1,281,167 training
images and 50,000 validation images. Images are embedded RGB 256x256 images;
fields are id, image, and label (class IDs 0–999). The dataset name
imagenet-latents-images is retained for loader compatibility; this release
contains images, not precomputed latent tensors.
ImageNet's original terms apply. This is a processed research release, not an ImageNet-endorsed distribution or a new license for the underlying images. Users must accept the ImageNet Terms of Access. Credit: Deng et al., ImageNet: A Large-Scale Hierarchical Image Database, CVPR 2009; Russakovsky et al., ImageNet Large Scale Visual Recognition Challenge, IJCV 2015.
Use with Priming
Get the code and environment from Priming. Accept the access terms once on this page, then authenticate your machine:
hf auth login
python scripts/download_assets.py --group dataset --root /large-disk/Priming
The download preserves the repository-relative data/ and assets/ layout.
Use the revision and SHA256 manifest linked in the code's data setup guide
for the pinned release. Downloads require approximately 259 GB of storage.
Load the packed data with datasets.load_from_disk, not load_dataset:
from datasets import load_from_disk
train = load_from_disk('/large-disk/Priming/data/imagenet/imagenet-latents-images')
validation = load_from_disk('/large-disk/Priming/data/imagenet/imagenet-latents-images/val')
The Arrow shards and their row order are preserved. Matching class indices
and OT calibration are included under assets/ot/representations/.
Operational paths were removed from the encoder provenance JSON files;
image bytes, labels, encoder weights, and calibration values are unchanged.
dataset_manifest.json identifies every released file by size and SHA256.
Do not reorder rows without rebuilding the class indices and calibration.
Generator checkpoints and evaluation assets are separate from this gated dataset. Sampling and the released FID/FDr5 evaluation do not require this training dataset.
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