Passenger555/DatasetDistillationCollection

Dataset Distillation Collection

This repository is a byte-preserving Parquet conversion of the assets audited in:

• synthetic_imagefolders.tar.gz
• other_pretrained_models.tar

It contains 481,090 synthetic images from 13 dataset-distillation methods and six ResNet-18 teacher checkpoints that were physically present in the teacher archive. The original compressed image bytes and checkpoint bytes are stored directly in Parquet binary columns; no image re-encoding or checkpoint rewriting was performed.

Configurations

images

One row per physical image. Important columns:

• method, dataset, ipc, class_id, and image_id identify the experiment cell and sample.
• image is the Hugging Face image feature backed by the original JPEG/PNG bytes.
• sample_weight is populated for all 73,800 WMDD rows and null for other methods.
• sha256 and byte_size validate the original compressed image payload.
• source_path and source_archive preserve provenance.

ipc is the nominal IPC recorded in the source folder. FreD and NCFM deliberately contain more physical images than classes × ipc; every physical image is retained.

teachers

One row per teacher checkpoint. The checkpoint column contains the original .pth bytes. The six included checkpoints are:

• CVDD ResNet-18 for CIFAR-10 and CIFAR-100
• G-VBSM ResNet-18 for CIFAR-10 and CIFAR-100
• WMDD ResNet-18 for TinyImageNet-200 and ImageNette

The source archive did not contain the following required assets, so this repository does not fabricate or substitute them:

• other_pretrained_models/GVBSM/tiny/ResNet18/squeeze_ResNet18.pth
• other_pretrained_models/SRe2L/cifar10/ckpt.pth
• other_pretrained_models/SRe2L/cifar100/ckpt.pth
• torchvision's official ImageNet ResNet-18 checkpoint (a runtime dependency in the project)

manifest

One row per uploaded image or teacher Parquet shard, including row counts, payload sizes, Parquet sizes, and SHA-256 digests.

Usage

from datasets import load_dataset

# Stream images without downloading the whole collection.
images = load_dataset(
    "Passenger555/DatasetDistillationCollection",
    "images",
    split="train",
    streaming=True,
)
first_image = next(iter(images))

# Restore a teacher checkpoint byte-for-byte.
teachers = load_dataset(
    "Passenger555/DatasetDistillationCollection",
    "teachers",
    split="train",
    streaming=True,
)
teacher = next(iter(teachers))
with open("teacher.pth", "wb") as handle:
    handle.write(teacher["checkpoint"])

Validation

The conversion was independently read back before upload. Validation covered all 481,090 image payloads and all six checkpoint payloads:

• recomputed SHA-256 matched every Parquet row;
• image row counts matched all 119 audited method/dataset/IPC groups;
• one image from each group decoded successfully;
• all WMDD sample weights were present and all non-WMDD weights were null;
• every shard matched the SHA-256 and file size recorded in the manifest.

See DD_ASSET_COMPLETENESS_CHECKLIST.md for the complete coverage matrix and conversion_summary.json for machine-readable counts and checksums.