File size: 4,913 Bytes
afecaea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | ---
pretty_name: Dataset Distillation Collection
tags:
- dataset-distillation
- computer-vision
- parquet
configs:
- config_name: images
default: true
data_files:
- split: train
path: data/images/**/*.parquet
- config_name: teachers
data_files:
- split: train
path: data/teachers/*.parquet
- config_name: manifest
data_files:
- split: train
path: data/manifest/*.parquet
dataset_info:
- config_name: images
features:
- name: method
dtype: string
- name: dataset
dtype: string
- name: ipc
dtype: int32
- name: class_id
dtype: int32
- name: image_id
dtype: int32
- name: source_path
dtype: string
- name: extension
dtype: string
- name: image
dtype: image
- name: sample_weight
dtype: float64
- name: sha256
dtype: string
- name: byte_size
dtype: int64
- name: source_archive
dtype: string
splits:
- name: train
num_examples: 481090
- config_name: teachers
features:
- name: provider
dtype: string
- name: dataset
dtype: string
- name: architecture
dtype: string
- name: used_by
sequence: string
- name: source_path
dtype: string
- name: checkpoint
dtype: binary
- name: sha256
dtype: string
- name: byte_size
dtype: int64
- name: source_archive
dtype: string
splits:
- name: train
num_examples: 6
- config_name: manifest
features:
- name: asset_type
dtype: string
- name: relative_path
dtype: string
- name: method_or_provider
dtype: string
- name: row_count
dtype: int64
- name: payload_bytes
dtype: int64
- name: parquet_bytes
dtype: int64
- name: sha256
dtype: string
splits:
- name: train
num_examples: 26
---
# 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
```python
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.
|