Datasets:

legolasflagstaff
/

RobustGenBench

@@ -1,142 +1,602 @@
 ---
-viewer: false
 tags:
-  - adversarial-robustness
-  - image-classification
-  - robustness-benchmark
 ---
 # RobustGenBench
-A benchmark for evaluating the adversarial robustness of zero-shot image classifiers across six fine-grained / domain-specific datasets and a range of threat models.
-A small **stratified sample** (2 images per class) is also available for quick inspection: 👉 https://huggingface.co/datasets/legolasflagstaff/RobustGenBench-sample
-## Structure
-```
-caltech101_processed.tar.zst
-fgvc-aircraft-2013b_processed.tar.zst
-flowers-102_processed.tar.zst
-oxford-iiit-pet_processed.tar.zst
-stanford_cars_processed.tar.zst
-uc-merced-land-use-dataset_processed.tar.zst
-class_names/
-  <dataset>.json                 ← integer-label → class-name mappings
-adversarial/
-  common/common_severity3/<dataset>__common_severity3_processed.tar.zst
-  random/linf_eps30_random_uniform/<dataset>__random_linf_eps30_random_uniform_processed.tar.zst
-  zeroshot_clip_vitb16_laion2b/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
-  zeroshot_clip_vith14_laion2b/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
-  zeroshot_metaclip_vith14_fullcc2_5b/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
-  zeroshot_siglip2_base_patch16_224/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
-  zeroshot_siglip2_so400m_patch14_384/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
-  zeroshot_siglip2_so400m_patch16_naflex/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
-  zeroshot_siglip2_so400m_patch16_naflex_patchify/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
-```
-Each archive is a `.tar.zst` containing flat-numbered PNGs and a `labels.csv`.
-### Clean archives
-```
-<dataset>_processed.tar.zst
-├── train/
-│   ├── 00000.png
-│   ├── 00001.png
-│   ├── ...
-│   └─�� labels.csv
-├── val/
-│   ├── 00000.png
-│   ├── ...
-│   └── labels.csv
-├── test/
-│   ├── 00000.png
-│   ├── ...
-│   └── labels.csv
-└── metadata.json                ← split counts and number of classes N
-```
-### Adversarial archives
 ```
-<dataset>__<threat_model>_processed.tar.zst
-└── test/
-    ├── 00000.png
-    ├── ...
-    └── labels.csv
 ```
-Filenames are aligned across all archives: `test/00000.png` in every adversarial archive corresponds to the same source image (and same label) used to generate the perturbation. `labels.csv` provides the `filename,label` mapping with integer class indices; resolve to class names via `class_names/<dataset>.json`.
 ## Datasets
-| Dataset | Classes | Test size |
 |---|---|---|
-| Caltech101 | 101 | 1000 |
-| FGVC-Aircraft 2013b | 100 | 1000 |
-| Oxford Flowers 102 | 102 | 1000 |
-| Oxford-IIIT Pet | 37 | 1000 |
-| Stanford Cars | 196 | 1000 |
-| UC Merced Land Use | 21 | 420 |
-## Threat models
-The `adversarial/` tree is organized by **surrogate model used to craft the attack**, then by **threat model**.
-**Untargeted attacks (AutoAttack standard, per surrogate):**
-- `linf_eps8_autoattack_standard` — L∞, ε = 8/255
-- `linf_eps30_autoattack_standard` — L∞, ε = 30/255
-- `l2_eps2_autoattack_standard` — L2, ε = 2
-- `l2_eps8_autoattack_standard` — L2, ε = 8
-- `l1_eps75_autoattack_standard` — L1, ε = 75
-- `l1_eps300_autoattack_standard` — L1, ε = 300
-**Surrogate-agnostic baselines:**
-- `common/common_severity3` — common corruption suite at severity 3
-- `random/linf_eps30_random_uniform` — random uniform L∞ noise at ε = 30/255
-## Loading
-```python
-import tarfile, io, csv
-import zstandard as zstd
-from PIL import Image
-from huggingface_hub import hf_hub_download
-archive = hf_hub_download(
-    repo_id="legolasflagstaff/RobustGenBench",
-    repo_type="dataset",
-    filename="uc-merced-land-use-dataset_processed.tar.zst",
-)
-with open(archive, "rb") as f:
-    buf = io.BytesIO(zstd.ZstdDecompressor().stream_reader(f).read())
-with tarfile.open(fileobj=buf, mode="r:") as tar:
-    images = {}
-    for m in tar.getmembers():
-        if m.name.startswith("test/") and m.name.endswith(".png"):
-            images[m.name] = Image.open(io.BytesIO(tar.extractfile(m).read())).convert("RGB")
-    labels_f = tar.extractfile(tar.getmember("test/labels.csv"))
-    labels = list(csv.DictReader(io.TextIOWrapper(labels_f)))
-print(f"Loaded {len(images)} images, {len(labels)} label rows")
-```
-## Citation
-If you use RobustGenBench in your work, please cite:
 ```bibtex
-@inproceedings{robustgenbench2025,
-  title  = {RobustGenBench: ...},
-  author = {...},
-  year   = {2025},
 }
 ```
-## License
-[Specify license here — e.g. CC-BY-4.0, or per-dataset license inheritance.]

 ---
+license: cc-by-nc-4.0
+language:
+- en
+pretty_name: RobustGenBench
+size_categories:
+- 100K<n<1M
+task_categories:
+- image-classification
+task_ids:
+- multi-class-image-classification
 tags:
+- adversarial-robustness
+- common-corruptions
+- robustness
+- benchmark
+- transfer-attacks
+- vision-language-models
+- autoattack
+configs:
+- config_name: caltech101
+  default: true
+  data_files:
+  - split: train
+    path: data/caltech101/train-*.parquet
+  - split: validation
+    path: data/caltech101/validation-*.parquet
+  - split: test
+    path: data/caltech101/test-*.parquet
+  - split: common
+    path: data/caltech101/common-*.parquet
+  - split: random_linf_30
+    path: data/caltech101/random_linf_30-*.parquet
+  - split: clip_vitb16_l1_75
+    path: data/caltech101/clip_vitb16_l1_75-*.parquet
+  - split: clip_vitb16_l1_300
+    path: data/caltech101/clip_vitb16_l1_300-*.parquet
+  - split: clip_vitb16_l2_2
+    path: data/caltech101/clip_vitb16_l2_2-*.parquet
+  - split: clip_vitb16_l2_8
+    path: data/caltech101/clip_vitb16_l2_8-*.parquet
+  - split: clip_vitb16_linf_4
+    path: data/caltech101/clip_vitb16_linf_4-*.parquet
+  - split: clip_vitb16_linf_8
+    path: data/caltech101/clip_vitb16_linf_8-*.parquet
+  - split: clip_vitb16_linf_30
+    path: data/caltech101/clip_vitb16_linf_30-*.parquet
+  - split: clip_vith14_l1_75
+    path: data/caltech101/clip_vith14_l1_75-*.parquet
+  - split: clip_vith14_l1_300
+    path: data/caltech101/clip_vith14_l1_300-*.parquet
+  - split: clip_vith14_l2_2
+    path: data/caltech101/clip_vith14_l2_2-*.parquet
+  - split: clip_vith14_l2_8
+    path: data/caltech101/clip_vith14_l2_8-*.parquet
+  - split: clip_vith14_linf_4
+    path: data/caltech101/clip_vith14_linf_4-*.parquet
+  - split: clip_vith14_linf_8
+    path: data/caltech101/clip_vith14_linf_8-*.parquet
+  - split: clip_vith14_linf_30
+    path: data/caltech101/clip_vith14_linf_30-*.parquet
+  - split: metaclip_vith14_l1_75
+    path: data/caltech101/metaclip_vith14_l1_75-*.parquet
+  - split: metaclip_vith14_l1_300
+    path: data/caltech101/metaclip_vith14_l1_300-*.parquet
+  - split: metaclip_vith14_l2_2
+    path: data/caltech101/metaclip_vith14_l2_2-*.parquet
+  - split: metaclip_vith14_l2_8
+    path: data/caltech101/metaclip_vith14_l2_8-*.parquet
+  - split: metaclip_vith14_linf_4
+    path: data/caltech101/metaclip_vith14_linf_4-*.parquet
+  - split: metaclip_vith14_linf_8
+    path: data/caltech101/metaclip_vith14_linf_8-*.parquet
+  - split: metaclip_vith14_linf_30
+    path: data/caltech101/metaclip_vith14_linf_30-*.parquet
+  - split: siglip2_so400m_l1_75
+    path: data/caltech101/siglip2_so400m_l1_75-*.parquet
+  - split: siglip2_so400m_l1_300
+    path: data/caltech101/siglip2_so400m_l1_300-*.parquet
+  - split: siglip2_so400m_l2_2
+    path: data/caltech101/siglip2_so400m_l2_2-*.parquet
+  - split: siglip2_so400m_l2_8
+    path: data/caltech101/siglip2_so400m_l2_8-*.parquet
+  - split: siglip2_so400m_linf_4
+    path: data/caltech101/siglip2_so400m_linf_4-*.parquet
+  - split: siglip2_so400m_linf_8
+    path: data/caltech101/siglip2_so400m_linf_8-*.parquet
+  - split: siglip2_so400m_linf_30
+    path: data/caltech101/siglip2_so400m_linf_30-*.parquet
+  - split: siglip2_base_linf_30
+    path: data/caltech101/siglip2_base_linf_30-*.parquet
+- config_name: fgvc-aircraft-2013b
+  data_files:
+  - split: train
+    path: data/fgvc-aircraft-2013b/train-*.parquet
+  - split: validation
+    path: data/fgvc-aircraft-2013b/validation-*.parquet
+  - split: test
+    path: data/fgvc-aircraft-2013b/test-*.parquet
+  - split: common
+    path: data/fgvc-aircraft-2013b/common-*.parquet
+  - split: random_linf_30
+    path: data/fgvc-aircraft-2013b/random_linf_30-*.parquet
+  - split: clip_vitb16_l1_75
+    path: data/fgvc-aircraft-2013b/clip_vitb16_l1_75-*.parquet
+  - split: clip_vitb16_l1_300
+    path: data/fgvc-aircraft-2013b/clip_vitb16_l1_300-*.parquet
+  - split: clip_vitb16_l2_2
+    path: data/fgvc-aircraft-2013b/clip_vitb16_l2_2-*.parquet
+  - split: clip_vitb16_l2_8
+    path: data/fgvc-aircraft-2013b/clip_vitb16_l2_8-*.parquet
+  - split: clip_vitb16_linf_4
+    path: data/fgvc-aircraft-2013b/clip_vitb16_linf_4-*.parquet
+  - split: clip_vitb16_linf_8
+    path: data/fgvc-aircraft-2013b/clip_vitb16_linf_8-*.parquet
+  - split: clip_vitb16_linf_30
+    path: data/fgvc-aircraft-2013b/clip_vitb16_linf_30-*.parquet
+  - split: clip_vith14_l1_75
+    path: data/fgvc-aircraft-2013b/clip_vith14_l1_75-*.parquet
+  - split: clip_vith14_l1_300
+    path: data/fgvc-aircraft-2013b/clip_vith14_l1_300-*.parquet
+  - split: clip_vith14_l2_2
+    path: data/fgvc-aircraft-2013b/clip_vith14_l2_2-*.parquet
+  - split: clip_vith14_l2_8
+    path: data/fgvc-aircraft-2013b/clip_vith14_l2_8-*.parquet
+  - split: clip_vith14_linf_4
+    path: data/fgvc-aircraft-2013b/clip_vith14_linf_4-*.parquet
+  - split: clip_vith14_linf_8
+    path: data/fgvc-aircraft-2013b/clip_vith14_linf_8-*.parquet
+  - split: clip_vith14_linf_30
+    path: data/fgvc-aircraft-2013b/clip_vith14_linf_30-*.parquet
+  - split: metaclip_vith14_l1_75
+    path: data/fgvc-aircraft-2013b/metaclip_vith14_l1_75-*.parquet
+  - split: metaclip_vith14_l1_300
+    path: data/fgvc-aircraft-2013b/metaclip_vith14_l1_300-*.parquet
+  - split: metaclip_vith14_l2_2
+    path: data/fgvc-aircraft-2013b/metaclip_vith14_l2_2-*.parquet
+  - split: metaclip_vith14_l2_8
+    path: data/fgvc-aircraft-2013b/metaclip_vith14_l2_8-*.parquet
+  - split: metaclip_vith14_linf_4
+    path: data/fgvc-aircraft-2013b/metaclip_vith14_linf_4-*.parquet
+  - split: metaclip_vith14_linf_8
+    path: data/fgvc-aircraft-2013b/metaclip_vith14_linf_8-*.parquet
+  - split: metaclip_vith14_linf_30
+    path: data/fgvc-aircraft-2013b/metaclip_vith14_linf_30-*.parquet
+  - split: siglip2_base_linf_30
+    path: data/fgvc-aircraft-2013b/siglip2_base_linf_30-*.parquet
+- config_name: flowers-102
+  data_files:
+  - split: train
+    path: data/flowers-102/train-*.parquet
+  - split: validation
+    path: data/flowers-102/validation-*.parquet
+  - split: test
+    path: data/flowers-102/test-*.parquet
+  - split: common
+    path: data/flowers-102/common-*.parquet
+  - split: random_linf_30
+    path: data/flowers-102/random_linf_30-*.parquet
+  - split: clip_vitb16_l1_75
+    path: data/flowers-102/clip_vitb16_l1_75-*.parquet
+  - split: clip_vitb16_l1_300
+    path: data/flowers-102/clip_vitb16_l1_300-*.parquet
+  - split: clip_vitb16_l2_2
+    path: data/flowers-102/clip_vitb16_l2_2-*.parquet
+  - split: clip_vitb16_l2_8
+    path: data/flowers-102/clip_vitb16_l2_8-*.parquet
+  - split: clip_vitb16_linf_4
+    path: data/flowers-102/clip_vitb16_linf_4-*.parquet
+  - split: clip_vitb16_linf_8
+    path: data/flowers-102/clip_vitb16_linf_8-*.parquet
+  - split: clip_vitb16_linf_30
+    path: data/flowers-102/clip_vitb16_linf_30-*.parquet
+  - split: clip_vith14_l1_75
+    path: data/flowers-102/clip_vith14_l1_75-*.parquet
+  - split: clip_vith14_l1_300
+    path: data/flowers-102/clip_vith14_l1_300-*.parquet
+  - split: clip_vith14_l2_2
+    path: data/flowers-102/clip_vith14_l2_2-*.parquet
+  - split: clip_vith14_l2_8
+    path: data/flowers-102/clip_vith14_l2_8-*.parquet
+  - split: clip_vith14_linf_4
+    path: data/flowers-102/clip_vith14_linf_4-*.parquet
+  - split: clip_vith14_linf_8
+    path: data/flowers-102/clip_vith14_linf_8-*.parquet
+  - split: clip_vith14_linf_30
+    path: data/flowers-102/clip_vith14_linf_30-*.parquet
+  - split: metaclip_vith14_l1_75
+    path: data/flowers-102/metaclip_vith14_l1_75-*.parquet
+  - split: metaclip_vith14_l1_300
+    path: data/flowers-102/metaclip_vith14_l1_300-*.parquet
+  - split: metaclip_vith14_l2_2
+    path: data/flowers-102/metaclip_vith14_l2_2-*.parquet
+  - split: metaclip_vith14_l2_8
+    path: data/flowers-102/metaclip_vith14_l2_8-*.parquet
+  - split: metaclip_vith14_linf_4
+    path: data/flowers-102/metaclip_vith14_linf_4-*.parquet
+  - split: metaclip_vith14_linf_8
+    path: data/flowers-102/metaclip_vith14_linf_8-*.parquet
+  - split: metaclip_vith14_linf_30
+    path: data/flowers-102/metaclip_vith14_linf_30-*.parquet
+  - split: siglip2_so400m_l1_75
+    path: data/flowers-102/siglip2_so400m_l1_75-*.parquet
+  - split: siglip2_so400m_l1_300
+    path: data/flowers-102/siglip2_so400m_l1_300-*.parquet
+  - split: siglip2_so400m_l2_2
+    path: data/flowers-102/siglip2_so400m_l2_2-*.parquet
+  - split: siglip2_so400m_l2_8
+    path: data/flowers-102/siglip2_so400m_l2_8-*.parquet
+  - split: siglip2_so400m_linf_4
+    path: data/flowers-102/siglip2_so400m_linf_4-*.parquet
+  - split: siglip2_so400m_linf_8
+    path: data/flowers-102/siglip2_so400m_linf_8-*.parquet
+  - split: siglip2_so400m_linf_30
+    path: data/flowers-102/siglip2_so400m_linf_30-*.parquet
+  - split: siglip2_base_linf_30
+    path: data/flowers-102/siglip2_base_linf_30-*.parquet
+- config_name: oxford-iiit-pet
+  data_files:
+  - split: train
+    path: data/oxford-iiit-pet/train-*.parquet
+  - split: validation
+    path: data/oxford-iiit-pet/validation-*.parquet
+  - split: test
+    path: data/oxford-iiit-pet/test-*.parquet
+  - split: common
+    path: data/oxford-iiit-pet/common-*.parquet
+  - split: random_linf_30
+    path: data/oxford-iiit-pet/random_linf_30-*.parquet
+  - split: clip_vitb16_l1_75
+    path: data/oxford-iiit-pet/clip_vitb16_l1_75-*.parquet
+  - split: clip_vitb16_l1_300
+    path: data/oxford-iiit-pet/clip_vitb16_l1_300-*.parquet
+  - split: clip_vitb16_l2_2
+    path: data/oxford-iiit-pet/clip_vitb16_l2_2-*.parquet
+  - split: clip_vitb16_l2_8
+    path: data/oxford-iiit-pet/clip_vitb16_l2_8-*.parquet
+  - split: clip_vitb16_linf_4
+    path: data/oxford-iiit-pet/clip_vitb16_linf_4-*.parquet
+  - split: clip_vitb16_linf_8
+    path: data/oxford-iiit-pet/clip_vitb16_linf_8-*.parquet
+  - split: clip_vitb16_linf_30
+    path: data/oxford-iiit-pet/clip_vitb16_linf_30-*.parquet
+  - split: clip_vith14_l1_75
+    path: data/oxford-iiit-pet/clip_vith14_l1_75-*.parquet
+  - split: clip_vith14_l1_300
+    path: data/oxford-iiit-pet/clip_vith14_l1_300-*.parquet
+  - split: clip_vith14_l2_2
+    path: data/oxford-iiit-pet/clip_vith14_l2_2-*.parquet
+  - split: clip_vith14_l2_8
+    path: data/oxford-iiit-pet/clip_vith14_l2_8-*.parquet
+  - split: clip_vith14_linf_4
+    path: data/oxford-iiit-pet/clip_vith14_linf_4-*.parquet
+  - split: clip_vith14_linf_8
+    path: data/oxford-iiit-pet/clip_vith14_linf_8-*.parquet
+  - split: clip_vith14_linf_30
+    path: data/oxford-iiit-pet/clip_vith14_linf_30-*.parquet
+  - split: metaclip_vith14_l1_75
+    path: data/oxford-iiit-pet/metaclip_vith14_l1_75-*.parquet
+  - split: metaclip_vith14_l1_300
+    path: data/oxford-iiit-pet/metaclip_vith14_l1_300-*.parquet
+  - split: metaclip_vith14_l2_2
+    path: data/oxford-iiit-pet/metaclip_vith14_l2_2-*.parquet
+  - split: metaclip_vith14_l2_8
+    path: data/oxford-iiit-pet/metaclip_vith14_l2_8-*.parquet
+  - split: metaclip_vith14_linf_4
+    path: data/oxford-iiit-pet/metaclip_vith14_linf_4-*.parquet
+  - split: metaclip_vith14_linf_8
+    path: data/oxford-iiit-pet/metaclip_vith14_linf_8-*.parquet
+  - split: metaclip_vith14_linf_30
+    path: data/oxford-iiit-pet/metaclip_vith14_linf_30-*.parquet
+  - split: siglip2_base_linf_30
+    path: data/oxford-iiit-pet/siglip2_base_linf_30-*.parquet
+- config_name: stanford_cars
+  data_files:
+  - split: train
+    path: data/stanford_cars/train-*.parquet
+  - split: validation
+    path: data/stanford_cars/validation-*.parquet
+  - split: test
+    path: data/stanford_cars/test-*.parquet
+  - split: common
+    path: data/stanford_cars/common-*.parquet
+  - split: random_linf_30
+    path: data/stanford_cars/random_linf_30-*.parquet
+  - split: clip_vitb16_l1_75
+    path: data/stanford_cars/clip_vitb16_l1_75-*.parquet
+  - split: clip_vitb16_l1_300
+    path: data/stanford_cars/clip_vitb16_l1_300-*.parquet
+  - split: clip_vitb16_l2_2
+    path: data/stanford_cars/clip_vitb16_l2_2-*.parquet
+  - split: clip_vitb16_l2_8
+    path: data/stanford_cars/clip_vitb16_l2_8-*.parquet
+  - split: clip_vitb16_linf_4
+    path: data/stanford_cars/clip_vitb16_linf_4-*.parquet
+  - split: clip_vitb16_linf_8
+    path: data/stanford_cars/clip_vitb16_linf_8-*.parquet
+  - split: clip_vitb16_linf_30
+    path: data/stanford_cars/clip_vitb16_linf_30-*.parquet
+  - split: clip_vith14_l1_75
+    path: data/stanford_cars/clip_vith14_l1_75-*.parquet
+  - split: clip_vith14_l1_300
+    path: data/stanford_cars/clip_vith14_l1_300-*.parquet
+  - split: clip_vith14_l2_2
+    path: data/stanford_cars/clip_vith14_l2_2-*.parquet
+  - split: clip_vith14_l2_8
+    path: data/stanford_cars/clip_vith14_l2_8-*.parquet
+  - split: clip_vith14_linf_4
+    path: data/stanford_cars/clip_vith14_linf_4-*.parquet
+  - split: clip_vith14_linf_8
+    path: data/stanford_cars/clip_vith14_linf_8-*.parquet
+  - split: clip_vith14_linf_30
+    path: data/stanford_cars/clip_vith14_linf_30-*.parquet
+  - split: metaclip_vith14_l1_75
+    path: data/stanford_cars/metaclip_vith14_l1_75-*.parquet
+  - split: metaclip_vith14_l1_300
+    path: data/stanford_cars/metaclip_vith14_l1_300-*.parquet
+  - split: metaclip_vith14_l2_2
+    path: data/stanford_cars/metaclip_vith14_l2_2-*.parquet
+  - split: metaclip_vith14_l2_8
+    path: data/stanford_cars/metaclip_vith14_l2_8-*.parquet
+  - split: metaclip_vith14_linf_4
+    path: data/stanford_cars/metaclip_vith14_linf_4-*.parquet
+  - split: metaclip_vith14_linf_8
+    path: data/stanford_cars/metaclip_vith14_linf_8-*.parquet
+  - split: metaclip_vith14_linf_30
+    path: data/stanford_cars/metaclip_vith14_linf_30-*.parquet
+  - split: siglip2_base_linf_30
+    path: data/stanford_cars/siglip2_base_linf_30-*.parquet
+- config_name: uc-merced-land-use-dataset
+  data_files:
+  - split: train
+    path: data/uc-merced-land-use-dataset/train-*.parquet
+  - split: validation
+    path: data/uc-merced-land-use-dataset/validation-*.parquet
+  - split: test
+    path: data/uc-merced-land-use-dataset/test-*.parquet
+  - split: common
+    path: data/uc-merced-land-use-dataset/common-*.parquet
+  - split: random_linf_30
+    path: data/uc-merced-land-use-dataset/random_linf_30-*.parquet
+  - split: clip_vitb16_l1_75
+    path: data/uc-merced-land-use-dataset/clip_vitb16_l1_75-*.parquet
+  - split: clip_vitb16_l1_300
+    path: data/uc-merced-land-use-dataset/clip_vitb16_l1_300-*.parquet
+  - split: clip_vitb16_l2_2
+    path: data/uc-merced-land-use-dataset/clip_vitb16_l2_2-*.parquet
+  - split: clip_vitb16_l2_8
+    path: data/uc-merced-land-use-dataset/clip_vitb16_l2_8-*.parquet
+  - split: clip_vitb16_linf_4
+    path: data/uc-merced-land-use-dataset/clip_vitb16_linf_4-*.parquet
+  - split: clip_vitb16_linf_8
+    path: data/uc-merced-land-use-dataset/clip_vitb16_linf_8-*.parquet
+  - split: clip_vitb16_linf_30
+    path: data/uc-merced-land-use-dataset/clip_vitb16_linf_30-*.parquet
+  - split: clip_vith14_l1_75
+    path: data/uc-merced-land-use-dataset/clip_vith14_l1_75-*.parquet
+  - split: clip_vith14_l1_300
+    path: data/uc-merced-land-use-dataset/clip_vith14_l1_300-*.parquet
+  - split: clip_vith14_l2_2
+    path: data/uc-merced-land-use-dataset/clip_vith14_l2_2-*.parquet
+  - split: clip_vith14_l2_8
+    path: data/uc-merced-land-use-dataset/clip_vith14_l2_8-*.parquet
+  - split: clip_vith14_linf_4
+    path: data/uc-merced-land-use-dataset/clip_vith14_linf_4-*.parquet
+  - split: clip_vith14_linf_8
+    path: data/uc-merced-land-use-dataset/clip_vith14_linf_8-*.parquet
+  - split: clip_vith14_linf_30
+    path: data/uc-merced-land-use-dataset/clip_vith14_linf_30-*.parquet
+  - split: metaclip_vith14_l1_75
+    path: data/uc-merced-land-use-dataset/metaclip_vith14_l1_75-*.parquet
+  - split: metaclip_vith14_l1_300
+    path: data/uc-merced-land-use-dataset/metaclip_vith14_l1_300-*.parquet
+  - split: metaclip_vith14_l2_2
+    path: data/uc-merced-land-use-dataset/metaclip_vith14_l2_2-*.parquet
+  - split: metaclip_vith14_l2_8
+    path: data/uc-merced-land-use-dataset/metaclip_vith14_l2_8-*.parquet
+  - split: metaclip_vith14_linf_4
+    path: data/uc-merced-land-use-dataset/metaclip_vith14_linf_4-*.parquet
+  - split: metaclip_vith14_linf_8
+    path: data/uc-merced-land-use-dataset/metaclip_vith14_linf_8-*.parquet
+  - split: metaclip_vith14_linf_30
+    path: data/uc-merced-land-use-dataset/metaclip_vith14_linf_30-*.parquet
+  - split: siglip2_so400m_l1_75
+    path: data/uc-merced-land-use-dataset/siglip2_so400m_l1_75-*.parquet
+  - split: siglip2_so400m_l1_300
+    path: data/uc-merced-land-use-dataset/siglip2_so400m_l1_300-*.parquet
+  - split: siglip2_so400m_l2_2
+    path: data/uc-merced-land-use-dataset/siglip2_so400m_l2_2-*.parquet
+  - split: siglip2_so400m_l2_8
+    path: data/uc-merced-land-use-dataset/siglip2_so400m_l2_8-*.parquet
+  - split: siglip2_so400m_linf_4
+    path: data/uc-merced-land-use-dataset/siglip2_so400m_linf_4-*.parquet
+  - split: siglip2_so400m_linf_8
+    path: data/uc-merced-land-use-dataset/siglip2_so400m_linf_8-*.parquet
+  - split: siglip2_so400m_linf_30
+    path: data/uc-merced-land-use-dataset/siglip2_so400m_linf_30-*.parquet
+  - split: siglip2_base_linf_30
+    path: data/uc-merced-land-use-dataset/siglip2_base_linf_30-*.parquet
 ---
 # RobustGenBench
+**A standardized benchmark for robust generalization of image classifiers across common and adversarial ($\ell_1, \ell_2, \ell_\infty$) perturbations.**
+📄 Paper · 💻 [Code](https://github.com/legolasflagstaff/robust_training) · 📜 [Citation](#citation)
+RobustGenBench evaluates how well a classifier keeps its accuracy on perturbation types it was not trained against. It provides six downstream classification datasets with fixed splits at 224×224 resolution, together with perturbed copies of every test set:
+- **Common corruptions** (11 types, severity 3): model-independent, the same images for every model.
+- **Random uniform noise** ($\ell_\infty \le 30/255$): a control for the adversarial perturbations.
+- **Transfer adversarial perturbations** (136,180 images): crafted with AutoAttack on 5 open-weight vision-language surrogates, across 7 budgets in $\ell_1$, $\ell_2$, $\ell_\infty$.
+Because the adversarial images are pre-computed, any model, including API-only systems such as vision-enabled LLMs, can be evaluated **by inference alone**, without GPU access. For white-box evaluation, attack your own model on the clean `test` split.
+In total: 5,420 clean test images and 147,020 perturbed test images.
+## Quick start
+```python
+from datasets import load_dataset
+# One config per dataset; one split per perturbation setting.
+clean = load_dataset("legolasflagstaff/RobustGenBench", "uc-merced-land-use-dataset", split="test")
+adv   = load_dataset("legolasflagstaff/RobustGenBench", "uc-merced-land-use-dataset", split="clip_vith14_linf_30")
+row = adv[0]
+row["image"]                                  # PIL.Image, 224×224 RGB
+adv.features["label"].int2str(row["label"])   # class name
+row["image_id"]                               # aligns with the clean image of the same id
 ```
+Evaluate a classifier on every perturbation setting of one dataset:
+```python
+from datasets import load_dataset, get_dataset_split_names
+repo, cfg = "legolasflagstaff/RobustGenBench", "flowers-102"
+for split in get_dataset_split_names(repo, cfg):
+    if split in ("train", "validation"):
+        continue
+    ds = load_dataset(repo, cfg, split=split)
+    acc = sum(predict(x["image"]) == x["label"] for x in ds) / len(ds)   # your model here
+    print(f"{split:24s} {acc:.3f}")
 ```
+Splits are aligned by `image_id`: row *i* of every perturbed split is a perturbed version of row *i* of `test`, with the same label.
 ## Datasets
+| Config | Dataset | Domain | Granularity | Classes | Train | Validation | Test |
+|---|---|---|---|---|---|---|---|
+| `caltech101` | Caltech-101 | objects | coarse | 101 | 5,899 | 1,042 | 1,000 |
+| `fgvc-aircraft-2013b` | FGVC-Aircraft (2013b) | aircraft | fine | 100 | 3,334 | 3,333 | 1,000 |
+| `flowers-102` | Oxford Flowers-102 | flowers | fine | 102 | 1,020 | 1,020 | 1,000 |
+| `oxford-iiit-pet` | Oxford-IIIT Pet | pets | fine | 37 | 3,128 | 552 | 1,000 |
+| `stanford_cars` | Stanford Cars | cars | fine | 196 | 6,922 | 1,222 | 1,000 |
+| `uc-merced-land-use-dataset` | UC Merced Land Use | satellite | coarse | 21 | 1,428 | 252 | 420 |
+Test sets are standardized to at most 1,000 images by uniform sampling, preserving all classes. All images are resized to 224×224×3 and stored as lossless PNG.
+## Splits
+Each config (dataset) contains the following splits.
+| Split | Content |
+|---|---|
+| `train`, `validation` | Clean images, for fine-tuning. |
+| `test` | Clean test images, the reference for all perturbed splits. |
+| `common` | Each test image with one of 11 common corruptions at severity 3 (column `corruption`). |
+| `random_linf_30` | Uniform random noise with $\lVert\delta\rVert_\infty \le 30/255$. |
+| `<surrogate>_<norm>_<eps>` | Transfer adversarial images, e.g. `clip_vitb16_linf_30`, `metaclip_vith14_l1_300`. |
+### Common corruptions
+Shot noise, impulse noise, defocus blur, motion blur, zoom blur, snow, brightness, contrast, elastic transform, pixelate, JPEG compression, all at severity 3 of the ImageNet-C scale. Test image *i* receives corruption *i* mod 11; the assignment is deterministic, so every model is evaluated on identical (image, corruption) pairs.
+### Adversarial perturbations
+Perturbations are crafted with the standard AutoAttack ensemble (APGD-CE, APGD-T, FAB-T, Square) against zero-shot surrogate classifiers. Each surrogate predicts the class whose text embedding (`"a photo of a {class}"`) is closest to the image embedding. AutoAttack only perturbs images the surrogate classifies correctly; other images are copied unchanged (`is_modified = false`).
+| Norm | Budgets $\epsilon$ | Split suffix |
 |---|---|---|
+| $\ell_1$ | 75, 300 | `l1_75`, `l1_300` |
+| $\ell_2$ | 2, 8 | `l2_2`, `l2_8` |
+| $\ell_\infty$ | 4/255, 8/255, 30/255 | `linf_4`, `linf_8`, `linf_30` |
+Budgets are expressed on pixel values in $[0, 1]$; the `epsilon` column stores the exact value (e.g. 0.1176 for 30/255).
+| Surrogate prefix | Model | Checkpoint |
+|---|---|---|
+| `clip_vitb16` | CLIP ViT-B/16 | open_clip `ViT-B-16` / `laion2b_s34b_b88k` |
+| `clip_vith14` | CLIP ViT-H/14 | open_clip `ViT-H-14` / `laion2b_s32b_b79k` |
+| `metaclip_vith14` | MetaCLIP ViT-H/14 | open_clip `ViT-H-14-quickgelu` / `metaclip_fullcc` |
+| `siglip2_so400m` | SigLIP2 SO400M | `google/siglip2-so400m-patch14-384` |
+| `siglip2_base` | SigLIP2 base | `google/siglip2-base-patch16-224` |
+### Coverage
+SigLIP-family surrogates classify the fine-grained datasets poorly, and a surrogate that misclassifies the clean image provides no attack signal. SigLIP2 SO400M is therefore provided on the three coarse-grained datasets only. SigLIP2 base is provided at $\ell_\infty$ 30/255 only; on the fine-grained datasets it leaves most images unmodified (see `is_modified`).
+| Split | Caltech-101 | FGVC-Aircraft (2013b) | Oxford Flowers-102 | Oxford-IIIT Pet | Stanford Cars | UC Merced Land Use |
+|---|---|---|---|---|---|---|
+| `clip_vitb16_l1_75` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vitb16_l1_300` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vitb16_l2_2` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vitb16_l2_8` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vitb16_linf_4` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vitb16_linf_8` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vitb16_linf_30` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vith14_l1_75` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vith14_l1_300` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vith14_l2_2` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vith14_l2_8` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vith14_linf_4` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vith14_linf_8` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `clip_vith14_linf_30` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `metaclip_vith14_l1_75` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `metaclip_vith14_l1_300` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `metaclip_vith14_l2_2` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `metaclip_vith14_l2_8` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `metaclip_vith14_linf_4` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `metaclip_vith14_linf_8` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `metaclip_vith14_linf_30` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+| `siglip2_so400m_l1_75` | ✓ | – | ✓ | – | – | ✓ |
+| `siglip2_so400m_l1_300` | ✓ | – | ✓ | – | – | ✓ |
+| `siglip2_so400m_l2_2` | ✓ | – | ✓ | – | – | ✓ |
+| `siglip2_so400m_l2_8` | ✓ | – | ✓ | – | – | ✓ |
+| `siglip2_so400m_linf_4` | ✓ | – | ✓ | – | – | ✓ |
+| `siglip2_so400m_linf_8` | ✓ | – | ✓ | – | – | ✓ |
+| `siglip2_so400m_linf_30` | ✓ | – | ✓ | – | – | ✓ |
+| `siglip2_base_linf_30` | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
+## Data fields
+| Field | Type | Description |
+|---|---|---|
+| `image` | image | 224×224 RGB PNG. |
+| `label` | class label | Ground-truth class (use `features["label"].names` for names). |
+| `image_id` | int32 | Index of the source image within its split; aligns perturbed splits with `test`. |
+| `perturbation` | string | `clean`, `common_corruption_severity3`, `random_uniform`, or `transfer_autoattack`. |
+| `surrogate` | string | Surrogate used to craft the attack (null otherwise). |
+| `norm` | string | `l1`, `l2`, or `linf` (null for clean and common). |
+| `epsilon` | float32 | Perturbation budget (null for clean and common). |
+| `corruption` | string | Common corruption applied (`common` split only). |
+| `is_modified` | bool | Whether the image differs from its clean counterpart. |
+## Evaluation protocol
+RobustGenBench reports accuracy per (dataset, perturbation setting), aggregated with:
+- **Sum score**: sum of accuracies over all (dataset, setting) pairs. Pool-independent; use it to compare new models with published results.
+- **Borda score**: rank-based consistency across pairs, only meaningful within a fixed pool of models.
+The paper uses two sets of settings:
+- **Fine-tuned vision models (5 settings):** `test`, `common`, and white-box AutoAttack on the evaluated model at $\ell_1 = 75$, $\ell_2 = 2$, $\ell_\infty = 4/255$, crafted from the clean `test` split. Maximum sum score: 30.
+- **Black-box models, e.g. vision-enabled LLMs (9 settings):** `test`, `common`, and the 7 transfer budgets, taking the strongest surrogate per dataset and setting.
+## Intended use and limitations
+**Intended use:** held-out evaluation of the robustness of image classifiers, fine-tuned or zero-shot, including API-only models. Do not train on the test or perturbed splits.
+**Limitations**
+- Transfer attacks give an upper bound on robust accuracy. They are not a substitute for white-box or adaptive attacks when model access is available.
+- All surrogates are attention-based, so transfer to convolutional targets is weaker.
+- Budgets are not matched across norms; compare norms only at the intensities provided.
+- Common corruptions are evaluated at a single severity.
+- Coverage is limited to six source taxonomies; results do not imply robustness on open-world imagery.
+**Biases.** The source datasets inherit the biases of their collection, for example Western car models (Stanford Cars) and breeds common in the UK (Oxford-IIIT Pet). Some images may incidentally show people or license plates; no additional anonymization was performed.
+## License
+The perturbed images, splits, and metadata produced for RobustGenBench are released under **[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)**. This license does not override the terms of the source datasets: every image is a derivative of an upstream image whose rights remain with its owners. Users must comply with both licenses; where they differ, the more restrictive applies.
+| Source | Homepage | Upstream terms |
+|---|---|---|
+| Caltech-101 | [https://data.caltech.edu/records/mzrjq-6wc02](https://data.caltech.edu/records/mzrjq-6wc02) | CC BY 4.0 |
+| FGVC-Aircraft (2013b) | [https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/](https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/) | non-commercial research use only |
+| Oxford Flowers-102 | [https://www.robots.ox.ac.uk/~vgg/data/flowers/102/](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/) | see upstream terms |
+| Oxford-IIIT Pet | [https://www.robots.ox.ac.uk/~vgg/data/pets/](https://www.robots.ox.ac.uk/~vgg/data/pets/) | CC BY-SA 4.0 |
+| Stanford Cars | [https://ai.stanford.edu/~jkrause/cars/car_dataset.html](https://ai.stanford.edu/~jkrause/cars/car_dataset.html) | see upstream terms (research use) |
+| UC Merced Land Use | [http://weegee.vision.ucmerced.edu/datasets/landuse.html](http://weegee.vision.ucmerced.edu/datasets/landuse.html) | USGS public-domain imagery |
+## Citation
 ```bibtex
+@inproceedings{heuillet2026robustgenbench,
+  title     = {RobustGenBench: A Benchmark for Robust Generalization to Adversarial and Common Perturbations, with Applications to Vision and Vision-Enabled Large Language Models},
+  author    = {Heuillet, Maxime and Ngnaw{\'e}, Jonas and Pequignot, Yann and Bhagwatkar, Rishika and Larouche, Alexandre and Gagn{\'e}, Christian and Rish, Irina and Ahmad, Ola and Durand, Audrey},
+  booktitle = {TODO},
+  year      = {2026}
 }
 ```
+Please also cite the six source datasets.
+## Versions
+- **v2.0** (current): Parquet layout with Dataset Viewer support; restricted to the surrogates reported in the paper.
+- **v1.0**: original `.tar.zst` archives, kept for reproducibility under the git tag [`v1.0-archives`](https://huggingface.co/datasets/legolasflagstaff/RobustGenBench/tree/v1.0-archives).

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