diff --git a/README.md b/README.md index 96a77af341d891763eab7aca55f8d1b8b10f94f0..4e43802b1d0501158909de35c9e1b59cc371f00c 100644 --- a/README.md +++ b/README.md @@ -1,142 +1,602 @@ --- -viewer: false +license: cc-by-nc-4.0 +language: +- en +pretty_name: RobustGenBench +size_categories: +- 100K.json โ† integer-label โ†’ class-name mappings - -adversarial/ - common/common_severity3/__common_severity3_processed.tar.zst - random/linf_eps30_random_uniform/__random_linf_eps30_random_uniform_processed.tar.zst - zeroshot_clip_vitb16_laion2b//___processed.tar.zst - zeroshot_clip_vith14_laion2b//___processed.tar.zst - zeroshot_metaclip_vith14_fullcc2_5b//___processed.tar.zst - zeroshot_siglip2_base_patch16_224//___processed.tar.zst - zeroshot_siglip2_so400m_patch14_384//___processed.tar.zst - zeroshot_siglip2_so400m_patch16_naflex//___processed.tar.zst - zeroshot_siglip2_so400m_patch16_naflex_patchify//___processed.tar.zst -``` +- **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 $\ell_1$, $\ell_2$, $\ell_\infty$. -Each archive is a `.tar.zst` containing flat-numbered PNGs and a `labels.csv`. +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. -### Clean archives +In total: 5,420 clean test images and 147,020 perturbed test images. -``` -_processed.tar.zst -โ”œโ”€โ”€ train/ -โ”‚ โ”œโ”€โ”€ 00000.png -โ”‚ โ”œโ”€โ”€ 00001.png -โ”‚ โ”œโ”€โ”€ ... -โ”‚ โ””โ”€โ”€ labels.csv -โ”œโ”€โ”€ val/ -โ”‚ โ”œโ”€โ”€ 00000.png -โ”‚ โ”œโ”€โ”€ ... -โ”‚ โ””โ”€โ”€ labels.csv -โ”œโ”€โ”€ test/ -โ”‚ โ”œโ”€โ”€ 00000.png -โ”‚ โ”œโ”€โ”€ ... -โ”‚ โ””โ”€โ”€ labels.csv -โ””โ”€โ”€ metadata.json โ† split counts and number of classes N -``` +## Quick start + +```python +from datasets import load_dataset -### Adversarial archives +# 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, 224ร—224 RGB +adv.features["label"].int2str(row["label"]) # class name +row["image_id"] # aligns with the clean image of the same id ``` -___processed.tar.zst -โ””โ”€โ”€ test/ - โ”œโ”€โ”€ 00000.png - โ”œโ”€โ”€ ... - โ””โ”€โ”€ labels.csv + +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}") ``` -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/.json`. +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 -| Dataset | Classes | Test size | +| 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 224ร—224ร—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 $\lVert\delta\rVert_\infty \le 30/255$. | +| `__` | 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 | |---|---|---| -| 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 | +| $\ell_1$ | 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` | -## Threat models +Budgets are expressed on pixel values in $[0, 1]$; the `epsilon` column stores the exact value (e.g. 0.1176 for 30/255). -The `adversarial/` tree is organized by **surrogate model used to craft the attack**, then by **threat model**. +| 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` | -**Untargeted attacks (AutoAttack standard, per surrogate):** -- `linf_eps8_autoattack_standard` โ€” Lโˆž, ฮต = 8/255 -- `linf_eps30_autoattack_standard` โ€” Lโˆž, ฮต = 30/255 -- `l2_eps2_autoattack_standard` โ€” L2, ฮต = 2 -- `l2_eps8_autoattack_standard` โ€” L2, ฮต = 8 -- `l1_eps75_autoattack_standard` โ€” L1, ฮต = 75 -- `l1_eps300_autoattack_standard` โ€” L1, ฮต = 300 +### Coverage -**Surrogate-agnostic baselines:** -- `common/common_severity3` โ€” common corruption suite at severity 3 -- `random/linf_eps30_random_uniform` โ€” random uniform Lโˆž noise at ฮต = 30/255 +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`). -## Loading +| Split | Caltech-101 | FGVC-Aircraft (2013b) | Oxford Flowers-102 | Oxford-IIIT Pet | Stanford Cars | UC Merced Land Use | +|---|---|---|---|---|---|---| +| `clip_vitb16_l1_75` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vitb16_l1_300` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vitb16_l2_2` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vitb16_l2_8` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vitb16_linf_4` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vitb16_linf_8` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vitb16_linf_30` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vith14_l1_75` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vith14_l1_300` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vith14_l2_2` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vith14_l2_8` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vith14_linf_4` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vith14_linf_8` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `clip_vith14_linf_30` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `metaclip_vith14_l1_75` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `metaclip_vith14_l1_300` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `metaclip_vith14_l2_2` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `metaclip_vith14_l2_8` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `metaclip_vith14_linf_4` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `metaclip_vith14_linf_8` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `metaclip_vith14_linf_30` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| `siglip2_so400m_l1_75` | โœ“ | โ€“ | โœ“ | โ€“ | โ€“ | โœ“ | +| `siglip2_so400m_l1_300` | โœ“ | โ€“ | โœ“ | โ€“ | โ€“ | โœ“ | +| `siglip2_so400m_l2_2` | โœ“ | โ€“ | โœ“ | โ€“ | โ€“ | โœ“ | +| `siglip2_so400m_l2_8` | โœ“ | โ€“ | โœ“ | โ€“ | โ€“ | โœ“ | +| `siglip2_so400m_linf_4` | โœ“ | โ€“ | โœ“ | โ€“ | โ€“ | โœ“ | +| `siglip2_so400m_linf_8` | โœ“ | โ€“ | โœ“ | โ€“ | โ€“ | โœ“ | +| `siglip2_so400m_linf_30` | โœ“ | โ€“ | โœ“ | โ€“ | โ€“ | โœ“ | +| `siglip2_base_linf_30` | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | -```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") -``` +## Data fields -## Citation +| Field | Type | Description | +|---|---|---| +| `image` | image | 224ร—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. -If you use RobustGenBench in your work, please cite: +**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{robustgenbench2025, - title = {RobustGenBench: ...}, - author = {...}, - year = {2025}, +@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} } ``` -## License +Please also cite the six source datasets. + +## Versions -[Specify license here โ€” e.g. CC-BY-4.0, or per-dataset license inheritance.] \ No newline at end of file +- **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). diff --git a/adversarial/common/common_severity3/caltech101__common_severity3_processed.tar.zst b/adversarial/common/common_severity3/caltech101__common_severity3_processed.tar.zst deleted file mode 100644 index dd7ecd263a9386b9d7335836e99ef1a61b402907..0000000000000000000000000000000000000000 --- a/adversarial/common/common_severity3/caltech101__common_severity3_processed.tar.zst +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:24369126458e46bb32acce4ae982591fd6a4b3695e483ce89532d8ea39cce587 -size 57648305 diff --git a/adversarial/common/common_severity3/fgvc-aircraft-2013b__common_severity3_processed.tar.zst b/adversarial/common/common_severity3/fgvc-aircraft-2013b__common_severity3_processed.tar.zst deleted file mode 100644 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sha256:ab7f61e7bbdb2c87d2d4efdb5ceaffcb94416912af916aeefc6ef52babe7b51b -size 543577356 diff --git a/class_names/caltech101.json b/class_names/caltech101.json deleted file mode 100644 index 2ae0705f03c0238ce89bb92bcf0789f96e6d41fd..0000000000000000000000000000000000000000 --- a/class_names/caltech101.json +++ /dev/null @@ -1,103 +0,0 @@ -{ - "0": "Faces", - "1": "Faces_easy", - "2": "Leopards", - "3": "Motorbikes", - "4": "accordion", - "5": "airplanes", - "6": "anchor", - "7": "ant", - "8": "barrel", - "9": "bass", - "10": "beaver", - "11": "binocular", - "12": "bonsai", - "13": "brain", - "14": "brontosaurus", - "15": "buddha", - "16": "butterfly", - "17": "camera", - "18": "cannon", - "19": "car_side", - "20": "ceiling_fan", - "21": "cellphone", - "22": "chair", - "23": "chandelier", - "24": "cougar_body", - "25": "cougar_face", - "26": "crab", - "27": "crayfish", - "28": "crocodile", - "29": "crocodile_head", - "30": "cup", - "31": "dalmatian", - "32": "dollar_bill", - "33": "dolphin", - "34": "dragonfly", - "35": "electric_guitar", - "36": "elephant", - "37": "emu", - "38": "euphonium", - "39": "ewer", - "40": "ferry", - "41": "flamingo", - "42": "flamingo_head", - "43": "garfield", - "44": "gerenuk", - "45": "gramophone", - "46": "grand_piano", - "47": "hawksbill", - "48": "headphone", - "49": "hedgehog", - "50": "helicopter", - "51": "ibis", - "52": "inline_skate", - "53": "joshua_tree", - "54": "kangaroo", - "55": "ketch", - "56": "lamp", - "57": "laptop", - "58": "llama", - "59": "lobster", - "60": "lotus", - "61": "mandolin", - "62": "mayfly", - "63": "menorah", - "64": "metronome", - "65": "minaret", - "66": "nautilus", - "67": "octopus", - "68": "okapi", - "69": "pagoda", - "70": "panda", - "71": "pigeon", - "72": "pizza", - "73": "platypus", - "74": "pyramid", - "75": "revolver", - "76": "rhino", - "77": "rooster", - "78": "saxophone", - "79": "schooner", - "80": "scissors", - "81": "scorpion", - "82": "sea_horse", - "83": "snoopy", - "84": "soccer_ball", - "85": "stapler", - "86": "starfish", - "87": "stegosaurus", - "88": "stop_sign", - "89": "strawberry", - "90": "sunflower", - "91": "tick", - "92": "trilobite", - "93": "umbrella", - "94": "watch", - "95": "water_lilly", - "96": "wheelchair", - "97": "wild_cat", - "98": "windsor_chair", - "99": "wrench", - "100": "yin_yang" -} \ No newline at end of file diff --git a/class_names/fgvc-aircraft-2013b.json b/class_names/fgvc-aircraft-2013b.json deleted file mode 100644 index e8201d8e9d95399f68d9321ac724cd175997e6f6..0000000000000000000000000000000000000000 --- a/class_names/fgvc-aircraft-2013b.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "0": "707-320", - "1": "727-200", - "2": "737-200", - "3": "737-300", - "4": "737-400", - "5": "737-500", - "6": "737-600", - "7": "737-700", - "8": "737-800", - "9": "737-900", - "10": "747-100", - "11": "747-200", - "12": "747-300", - "13": "747-400", - "14": "757-200", - "15": "757-300", - "16": "767-200", - "17": "767-300", - "18": "767-400", - "19": "777-200", - "20": "777-300", - "21": "A300B4", - "22": "A310", - "23": "A318", - "24": "A319", - "25": "A320", - "26": "A321", - "27": "A330-200", - "28": "A330-300", - "29": "A340-200", - "30": "A340-300", - "31": "A340-500", - "32": "A340-600", - "33": "A380", - "34": "ATR-42", - "35": "ATR-72", - "36": "An-12", - "37": "BAE 146-200", - "38": "BAE 146-300", - "39": "BAE-125", - "40": "Beechcraft 1900", - "41": "Boeing 717", - "42": "C-130", - "43": "C-47", - "44": "CRJ-200", - "45": "CRJ-700", - "46": "CRJ-900", - "47": "Cessna 172", - "48": "Cessna 208", - "49": "Cessna 525", - "50": "Cessna 560", - "51": "Challenger 600", - "52": "DC-10", - "53": "DC-3", - "54": "DC-6", - "55": "DC-8", - "56": "DC-9-30", - "57": "DH-82", - "58": "DHC-1", - "59": "DHC-6", - "60": "DHC-8-100", - "61": "DHC-8-300", - "62": "DR-400", - "63": "Dornier 328", - "64": "E-170", - "65": "E-190", - "66": "E-195", - "67": "EMB-120", - "68": "ERJ 135", - "69": "ERJ 145", - "70": "Embraer Legacy 600", - "71": "Eurofighter Typhoon", - "72": "F-16A/B", - "73": "F/A-18", - "74": "Falcon 2000", - "75": "Falcon 900", - "76": "Fokker 100", - "77": "Fokker 50", - "78": "Fokker 70", - "79": "Global Express", - "80": "Gulfstream IV", - "81": "Gulfstream V", - "82": "Hawk T1", - "83": "Il-76", - "84": "L-1011", - "85": "MD-11", - "86": "MD-80", - "87": "MD-87", - "88": "MD-90", - "89": "Metroliner", - "90": "Model B200", - "91": "PA-28", - "92": "SR-20", - "93": "Saab 2000", - "94": "Saab 340", - "95": "Spitfire", - "96": "Tornado", - "97": "Tu-134", - "98": "Tu-154", - "99": "Yak-42" -} \ No newline at end of file diff --git a/class_names/flowers-102.json b/class_names/flowers-102.json deleted file mode 100644 index c0ddb1ee2d5dc54e0fdf1b6bf8aff057fecac712..0000000000000000000000000000000000000000 --- a/class_names/flowers-102.json +++ /dev/null @@ -1,104 +0,0 @@ -{ - "0": "pink primrose", - "1": "hard-leaved pocket orchid", - "2": "canterbury bells", - "3": "sweet pea", - "4": "english marigold", - "5": "tiger lily", - "6": "moon orchid", - "7": "bird of paradise", - "8": "monkshood", - "9": "globe thistle", - "10": "snapdragon", - "11": "colt's foot", - "12": "king protea", - "13": "spear thistle", - "14": "yellow iris", - "15": "globe-flower", - "16": "purple coneflower", - "17": "peruvian lily", - "18": "balloon flower", - "19": "giant white arum lily", - "20": "fire lily", - "21": "pincushion flower", - "22": "fritillary", - "23": "red ginger", - "24": "grape hyacinth", - "25": "corn poppy", - "26": "prince of wales feathers", - "27": "stemless gentian", - "28": "artichoke", - "29": "sweet william", - "30": "carnation", - "31": "garden phlox", - "32": "love in the mist", - "33": "mexican aster", - "34": "alpine sea holly", - "35": "ruby-lipped cattleya", - "36": "cape flower", - "37": "great masterwort", - "38": "siam tulip", - "39": "lenten rose", - "40": "barbeton daisy", - "41": "daffodil", - "42": "sword lily", - "43": "poinsettia", - "44": "bolero deep blue", - "45": "wallflower", - "46": "marigold", - "47": "buttercup", - "48": "oxeye daisy", - "49": "common dandelion", - "50": "petunia", - "51": "wild pansy", - "52": "primula", - "53": "sunflower", - "54": "pelargonium", - "55": "bishop of llandaff", - "56": "gaura", - "57": "geranium", - "58": "orange dahlia", - "59": "pink-yellow dahlia", - "60": "cautleya spicata", - "61": "japanese anemone", - "62": "black-eyed susan", - "63": "silverbush", - "64": "californian poppy", - "65": "osteospermum", - "66": "spring crocus", - "67": "bearded iris", - "68": "windflower", - "69": "tree poppy", - "70": "gazania", - "71": "azalea", - "72": "water lily", - "73": "rose", - "74": "thorn apple", - "75": "morning glory", - "76": "passion flower", - "77": "lotus", - "78": "toad lily", - "79": "anthurium", - "80": "frangipani", - "81": "clematis", - "82": "hibiscus", - "83": "columbine", - "84": "desert-rose", - "85": "tree mallow", - "86": "magnolia", - "87": "cyclamen", - "88": "watercress", - "89": "canna lily", - "90": "hippeastrum", - "91": "bee balm", - "92": "ball moss", - "93": "foxglove", - "94": "bougainvillea", - "95": "camellia", - "96": "mallow", - "97": "mexican petunia", - "98": "bromelia", - "99": "blanket flower", - "100": "trumpet creeper", - "101": "blackberry lily" -} \ No newline at end of file diff --git a/class_names/oxford-iiit-pet.json b/class_names/oxford-iiit-pet.json deleted file mode 100644 index acec7590322d7df25ca0b6f7db1956108ed859a2..0000000000000000000000000000000000000000 --- a/class_names/oxford-iiit-pet.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "0": "Abyssinian", - "1": "American Bulldog", - "2": "American Pit Bull Terrier", - "3": "Basset Hound", - "4": "Beagle", - "5": "Bengal", - "6": "Birman", - "7": "Bombay", - "8": "Boxer", - "9": "British Shorthair", - "10": "Chihuahua", - "11": "Egyptian Mau", - "12": "English Cocker Spaniel", - "13": "English Setter", - "14": "German Shorthaired", - "15": "Great Pyrenees", - "16": "Havanese", - "17": "Japanese Chin", - "18": "Keeshond", - "19": "Leonberger", - "20": "Maine Coon", - "21": "Miniature Pinscher", - "22": "Newfoundland", - "23": "Persian", - "24": "Pomeranian", - "25": "Pug", - "26": "Ragdoll", - "27": "Russian Blue", - "28": "Saint Bernard", - "29": "Samoyed", - "30": "Scottish Terrier", - "31": "Shiba Inu", - "32": "Siamese", - "33": "Sphynx", - "34": "Staffordshire Bull Terrier", - "35": "Wheaten Terrier", - "36": "Yorkshire Terrier" -} \ No newline at end of file diff --git a/class_names/stanford_cars.json b/class_names/stanford_cars.json deleted file mode 100644 index ace4633adc943e7eaf09183dee64b4546abd21c5..0000000000000000000000000000000000000000 --- a/class_names/stanford_cars.json +++ /dev/null @@ -1,198 +0,0 @@ -{ - "0": "AM General Hummer SUV 2000", - "1": "Acura RL Sedan 2012", - "2": "Acura TL Sedan 2012", - "3": "Acura TL Type-S 2008", - "4": "Acura TSX Sedan 2012", - "5": "Acura Integra Type R 2001", - "6": "Acura ZDX Hatchback 2012", - "7": "Aston Martin V8 Vantage Convertible 2012", - "8": "Aston Martin V8 Vantage Coupe 2012", - "9": "Aston Martin Virage Convertible 2012", - "10": "Aston Martin Virage Coupe 2012", - "11": "Audi RS 4 Convertible 2008", - "12": "Audi A5 Coupe 2012", - "13": "Audi TTS Coupe 2012", - "14": "Audi R8 Coupe 2012", - "15": "Audi V8 Sedan 1994", - "16": "Audi 100 Sedan 1994", - "17": "Audi 100 Wagon 1994", - "18": "Audi TT Hatchback 2011", - "19": "Audi S6 Sedan 2011", - "20": "Audi S5 Convertible 2012", - "21": "Audi S5 Coupe 2012", - "22": "Audi S4 Sedan 2012", - "23": "Audi S4 Sedan 2007", - "24": "Audi TT RS Coupe 2012", - "25": "BMW ActiveHybrid 5 Sedan 2012", - "26": "BMW 1 Series Convertible 2012", - "27": "BMW 1 Series Coupe 2012", - "28": "BMW 3 Series Sedan 2012", - "29": "BMW 3 Series Wagon 2012", - "30": "BMW 6 Series Convertible 2007", - "31": "BMW X5 SUV 2007", - "32": "BMW X6 SUV 2012", - "33": "BMW M3 Coupe 2012", - "34": "BMW M5 Sedan 2010", - "35": "BMW M6 Convertible 2010", - "36": "BMW X3 SUV 2012", - "37": "BMW Z4 Convertible 2012", - "38": "Bentley Continental Supersports Conv. 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Sonic Sedan 2012", - "63": "Chevrolet Express Cargo Van 2007", - "64": "Chevrolet Avalanche Crew Cab 2012", - "65": "Chevrolet Cobalt SS 2010", - "66": "Chevrolet Malibu Hybrid Sedan 2010", - "67": "Chevrolet TrailBlazer SS 2009", - "68": "Chevrolet Silverado 2500HD Regular Cab 2012", - "69": "Chevrolet Silverado 1500 Classic Extended Cab 2007", - "70": "Chevrolet Express Van 2007", - "71": "Chevrolet Monte Carlo Coupe 2007", - "72": "Chevrolet Malibu Sedan 2007", - "73": "Chevrolet Silverado 1500 Extended Cab 2012", - "74": "Chevrolet Silverado 1500 Regular Cab 2012", - "75": "Chrysler Aspen SUV 2009", - "76": "Chrysler Sebring Convertible 2010", - "77": "Chrysler Town and Country Minivan 2012", - "78": "Chrysler 300 SRT-8 2010", - "79": "Chrysler Crossfire Convertible 2008", - "80": "Chrysler PT Cruiser Convertible 2008", - "81": "Daewoo Nubira Wagon 2002", - "82": "Dodge Caliber Wagon 2012", - "83": "Dodge Caliber Wagon 2007", - "84": "Dodge Caravan Minivan 1997", - "85": "Dodge Ram 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"Staffordshire Bull Terrier", + "Wheaten Terrier", + "Yorkshire Terrier" + ], + "splits": { + "train": { + "num_rows": 3128, + "num_shards": 1 + }, + "validation": { + "num_rows": 552, + "num_shards": 1 + }, + "test": { + "num_rows": 1000, + "num_shards": 1 + }, + "common": { + "num_rows": 1000, + "num_shards": 1 + }, + "random_linf_30": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_l1_75": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_l1_300": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_l2_2": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_l2_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_linf_4": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_linf_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_linf_30": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_l1_75": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_l1_300": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_l2_2": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_l2_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_linf_4": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_linf_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_linf_30": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_l1_75": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_l1_300": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_l2_2": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_l2_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_linf_4": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_linf_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_linf_30": { + "num_rows": 1000, + "num_shards": 1 + }, + "siglip2_base_linf_30": { + "num_rows": 1000, + "num_shards": 1 + } + } +} \ No newline at end of file diff --git a/manifests/stanford_cars.json b/manifests/stanford_cars.json new file mode 100644 index 0000000000000000000000000000000000000000..136ee7f30a7ef7090f3494e27504f21445de3d45 --- /dev/null +++ b/manifests/stanford_cars.json @@ -0,0 +1,311 @@ +{ + "num_classes": 196, + "class_names": [ + "AM General Hummer SUV 2000", + "Acura RL Sedan 2012", + "Acura TL Sedan 2012", + "Acura TL Type-S 2008", + "Acura TSX Sedan 2012", + "Acura Integra Type R 2001", + "Acura ZDX Hatchback 2012", + "Aston Martin V8 Vantage Convertible 2012", + "Aston Martin V8 Vantage Coupe 2012", + "Aston Martin Virage Convertible 2012", + "Aston Martin Virage Coupe 2012", + "Audi RS 4 Convertible 2008", + "Audi A5 Coupe 2012", + "Audi TTS Coupe 2012", + "Audi R8 Coupe 2012", + "Audi V8 Sedan 1994", + "Audi 100 Sedan 1994", + "Audi 100 Wagon 1994", + "Audi TT Hatchback 2011", + "Audi S6 Sedan 2011", + "Audi S5 Convertible 2012", + "Audi S5 Coupe 2012", + "Audi S4 Sedan 2012", + "Audi S4 Sedan 2007", + "Audi TT RS Coupe 2012", + "BMW ActiveHybrid 5 Sedan 2012", + "BMW 1 Series Convertible 2012", + "BMW 1 Series Coupe 2012", + "BMW 3 Series Sedan 2012", + "BMW 3 Series Wagon 2012", + "BMW 6 Series Convertible 2007", + "BMW X5 SUV 2007", + "BMW X6 SUV 2012", + "BMW M3 Coupe 2012", + "BMW M5 Sedan 2010", + "BMW M6 Convertible 2010", + "BMW X3 SUV 2012", + "BMW Z4 Convertible 2012", + "Bentley Continental Supersports Conv. Convertible 2012", + "Bentley Arnage Sedan 2009", + "Bentley Mulsanne Sedan 2011", + "Bentley Continental GT Coupe 2012", + "Bentley Continental GT Coupe 2007", + "Bentley Continental Flying Spur Sedan 2007", + "Bugatti Veyron 16.4 Convertible 2009", + "Bugatti Veyron 16.4 Coupe 2009", + "Buick Regal GS 2012", + "Buick Rainier SUV 2007", + "Buick Verano Sedan 2012", + "Buick Enclave SUV 2012", + "Cadillac CTS-V Sedan 2012", + "Cadillac SRX SUV 2012", + "Cadillac Escalade EXT Crew Cab 2007", + "Chevrolet Silverado 1500 Hybrid Crew Cab 2012", + "Chevrolet Corvette Convertible 2012", + "Chevrolet Corvette ZR1 2012", + "Chevrolet Corvette Ron Fellows Edition Z06 2007", + "Chevrolet Traverse SUV 2012", + "Chevrolet Camaro Convertible 2012", + "Chevrolet HHR SS 2010", + "Chevrolet Impala Sedan 2007", + "Chevrolet Tahoe Hybrid SUV 2012", + "Chevrolet Sonic Sedan 2012", + "Chevrolet Express Cargo Van 2007", + "Chevrolet Avalanche Crew Cab 2012", + "Chevrolet Cobalt SS 2010", + "Chevrolet Malibu Hybrid Sedan 2010", + "Chevrolet TrailBlazer SS 2009", + "Chevrolet Silverado 2500HD Regular Cab 2012", + "Chevrolet Silverado 1500 Classic Extended Cab 2007", + "Chevrolet Express Van 2007", + "Chevrolet Monte Carlo Coupe 2007", + "Chevrolet Malibu Sedan 2007", + "Chevrolet Silverado 1500 Extended Cab 2012", + "Chevrolet Silverado 1500 Regular Cab 2012", + "Chrysler Aspen SUV 2009", + "Chrysler Sebring Convertible 2010", + "Chrysler Town and Country Minivan 2012", + "Chrysler 300 SRT-8 2010", + "Chrysler Crossfire Convertible 2008", + "Chrysler PT Cruiser Convertible 2008", + "Daewoo Nubira Wagon 2002", + "Dodge Caliber Wagon 2012", + "Dodge Caliber Wagon 2007", + "Dodge Caravan Minivan 1997", + "Dodge Ram Pickup 3500 Crew Cab 2010", + "Dodge Ram Pickup 3500 Quad Cab 2009", + "Dodge Sprinter Cargo Van 2009", + "Dodge Journey SUV 2012", + "Dodge Dakota Crew Cab 2010", + "Dodge Dakota Club Cab 2007", + "Dodge Magnum Wagon 2008", + "Dodge Challenger SRT8 2011", + "Dodge Durango SUV 2012", + "Dodge Durango SUV 2007", + "Dodge Charger Sedan 2012", + "Dodge Charger SRT-8 2009", + "Eagle Talon Hatchback 1998", + "FIAT 500 Abarth 2012", + "FIAT 500 Convertible 2012", + "Ferrari FF Coupe 2012", + "Ferrari California Convertible 2012", + "Ferrari 458 Italia Convertible 2012", + "Ferrari 458 Italia Coupe 2012", + "Fisker Karma Sedan 2012", + "Ford F-450 Super Duty Crew Cab 2012", + "Ford Mustang Convertible 2007", + "Ford Freestar Minivan 2007", + "Ford Expedition EL SUV 2009", + "Ford Edge SUV 2012", + "Ford Ranger SuperCab 2011", + "Ford GT Coupe 2006", + "Ford F-150 Regular Cab 2012", + "Ford F-150 Regular Cab 2007", + "Ford Focus Sedan 2007", + "Ford E-Series Wagon Van 2012", + "Ford Fiesta Sedan 2012", + "GMC Terrain SUV 2012", + "GMC Savana Van 2012", + "GMC Yukon Hybrid SUV 2012", + "GMC Acadia SUV 2012", + "GMC Canyon Extended Cab 2012", + "Geo Metro Convertible 1993", + "HUMMER H3T Crew Cab 2010", + "HUMMER H2 SUT Crew Cab 2009", + "Honda Odyssey Minivan 2012", + "Honda Odyssey Minivan 2007", + "Honda Accord Coupe 2012", + "Honda Accord Sedan 2012", + "Hyundai Veloster Hatchback 2012", + "Hyundai Santa Fe SUV 2012", + "Hyundai Tucson SUV 2012", + "Hyundai Veracruz SUV 2012", + "Hyundai Sonata Hybrid Sedan 2012", + "Hyundai Elantra Sedan 2007", + "Hyundai Accent Sedan 2012", + "Hyundai Genesis Sedan 2012", + "Hyundai Sonata Sedan 2012", + "Hyundai Elantra Touring Hatchback 2012", + "Hyundai Azera Sedan 2012", + "Infiniti G Coupe IPL 2012", + "Infiniti QX56 SUV 2011", + "Isuzu Ascender SUV 2008", + "Jaguar XK XKR 2012", + "Jeep Patriot SUV 2012", + "Jeep Wrangler SUV 2012", + "Jeep Liberty SUV 2012", + "Jeep Grand Cherokee SUV 2012", + "Jeep Compass SUV 2012", + "Lamborghini Reventon Coupe 2008", + "Lamborghini Aventador Coupe 2012", + "Lamborghini Gallardo LP 570-4 Superleggera 2012", + "Lamborghini Diablo Coupe 2001", + "Land Rover Range Rover SUV 2012", + "Land Rover LR2 SUV 2012", + "Lincoln Town Car Sedan 2011", + "MINI Cooper Roadster Convertible 2012", + "Maybach Landaulet Convertible 2012", + "Mazda Tribute SUV 2011", + "McLaren MP4-12C Coupe 2012", + "Mercedes-Benz 300-Class Convertible 1993", + "Mercedes-Benz C-Class Sedan 2012", + "Mercedes-Benz SL-Class Coupe 2009", + "Mercedes-Benz E-Class Sedan 2012", + "Mercedes-Benz S-Class Sedan 2012", + "Mercedes-Benz Sprinter Van 2012", + "Mitsubishi Lancer Sedan 2012", + "Nissan Leaf Hatchback 2012", + "Nissan NV Passenger Van 2012", + "Nissan Juke Hatchback 2012", + "Nissan 240SX Coupe 1998", + "Plymouth Neon Coupe 1999", + "Porsche Panamera Sedan 2012", + "Ram C/V Cargo Van Minivan 2012", + "Rolls-Royce Phantom Drophead Coupe Convertible 2012", + "Rolls-Royce Ghost Sedan 2012", + "Rolls-Royce Phantom Sedan 2012", + "Scion xD Hatchback 2012", + "Spyker C8 Convertible 2009", + "Spyker C8 Coupe 2009", + "Suzuki Aerio Sedan 2007", + "Suzuki Kizashi Sedan 2012", + "Suzuki SX4 Hatchback 2012", + "Suzuki SX4 Sedan 2012", + "Tesla Model S Sedan 2012", + "Toyota Sequoia SUV 2012", + "Toyota Camry Sedan 2012", + "Toyota Corolla Sedan 2012", + "Toyota 4Runner SUV 2012", + "Volkswagen Golf Hatchback 2012", + "Volkswagen Golf Hatchback 1991", + "Volkswagen Beetle Hatchback 2012", + "Volvo C30 Hatchback 2012", + "Volvo 240 Sedan 1993", + "Volvo XC90 SUV 2007", + "smart fortwo Convertible 2012" + ], + "splits": { + "train": { + "num_rows": 6922, + "num_shards": 2 + }, + "validation": { + "num_rows": 1222, + "num_shards": 1 + }, + "test": { + "num_rows": 1000, + "num_shards": 1 + }, + "common": { + "num_rows": 1000, + "num_shards": 1 + }, + "random_linf_30": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_l1_75": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_l1_300": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_l2_2": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_l2_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_linf_4": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_linf_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vitb16_linf_30": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_l1_75": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_l1_300": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_l2_2": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_l2_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_linf_4": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_linf_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "clip_vith14_linf_30": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_l1_75": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_l1_300": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_l2_2": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_l2_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_linf_4": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_linf_8": { + "num_rows": 1000, + "num_shards": 1 + }, + "metaclip_vith14_linf_30": { + "num_rows": 1000, + "num_shards": 1 + }, + "siglip2_base_linf_30": { + "num_rows": 1000, + "num_shards": 1 + } + } +} \ No newline at end of file diff --git a/manifests/uc-merced-land-use-dataset.json b/manifests/uc-merced-land-use-dataset.json new file mode 100644 index 0000000000000000000000000000000000000000..dbf11362d462d1706ed1a19a9d4bd1284e7fcf47 --- /dev/null +++ b/manifests/uc-merced-land-use-dataset.json @@ -0,0 +1,164 @@ +{ + "num_classes": 21, + "class_names": [ + "agricultural", + "airplane", + "baseballdiamond", + "beach", + "buildings", + "chaparral", + "denseresidential", + "forest", + "freeway", + "golfcourse", + "harbor", + "intersection", + "mediumresidential", + "mobilehomepark", + "overpass", + "parkinglot", + "river", + "runway", + "sparseresidential", + "storagetanks", + "tenniscourt" + ], + "splits": { + "train": { + "num_rows": 1428, + "num_shards": 1 + }, + "validation": { + "num_rows": 252, + "num_shards": 1 + }, + "test": { + "num_rows": 420, + "num_shards": 1 + }, + "common": { + "num_rows": 420, + "num_shards": 1 + }, + "random_linf_30": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vitb16_l1_75": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vitb16_l1_300": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vitb16_l2_2": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vitb16_l2_8": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vitb16_linf_4": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vitb16_linf_8": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vitb16_linf_30": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vith14_l1_75": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vith14_l1_300": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vith14_l2_2": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vith14_l2_8": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vith14_linf_4": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vith14_linf_8": { + "num_rows": 420, + "num_shards": 1 + }, + "clip_vith14_linf_30": { + "num_rows": 420, + "num_shards": 1 + }, + "metaclip_vith14_l1_75": { + "num_rows": 420, + "num_shards": 1 + }, + "metaclip_vith14_l1_300": { + "num_rows": 420, + "num_shards": 1 + }, + "metaclip_vith14_l2_2": { + "num_rows": 420, + "num_shards": 1 + }, + "metaclip_vith14_l2_8": { + "num_rows": 420, + "num_shards": 1 + }, + "metaclip_vith14_linf_4": { + "num_rows": 420, + "num_shards": 1 + }, + "metaclip_vith14_linf_8": { + "num_rows": 420, + "num_shards": 1 + }, + "metaclip_vith14_linf_30": { + "num_rows": 420, + "num_shards": 1 + }, + "siglip2_so400m_l1_75": { + "num_rows": 420, + "num_shards": 1 + }, + "siglip2_so400m_l1_300": { + "num_rows": 420, + "num_shards": 1 + }, + "siglip2_so400m_l2_2": { + "num_rows": 420, + "num_shards": 1 + }, + "siglip2_so400m_l2_8": { + "num_rows": 420, + "num_shards": 1 + }, + "siglip2_so400m_linf_4": { + "num_rows": 420, + "num_shards": 1 + }, + "siglip2_so400m_linf_8": { + "num_rows": 420, + "num_shards": 1 + }, + "siglip2_so400m_linf_30": { + "num_rows": 420, + "num_shards": 1 + }, + "siglip2_base_linf_30": { + "num_rows": 420, + "num_shards": 1 + } + } +} \ No newline at end of file diff --git a/oxford-iiit-pet_processed.tar.zst b/oxford-iiit-pet_processed.tar.zst deleted file mode 100644 index f0c98f820b7069a12444079ce4eab495a77131ba..0000000000000000000000000000000000000000 --- a/oxford-iiit-pet_processed.tar.zst +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:98638faccc3bb78236cd7c7dd4b70e47f8bb44839608a7e3310c0966b233b2fd -size 385792741 diff --git a/scripts/build_parquet.py b/scripts/build_parquet.py new file mode 100644 index 0000000000000000000000000000000000000000..214951ae07f273041703e70b999dc85547f74c0d --- /dev/null +++ b/scripts/build_parquet.py @@ -0,0 +1,163 @@ +""" +Convert the v1.0 RobustGenBench archives (.tar.zst) into the v2.0 Parquet layout. + +Layout: data//-XXXXX-of-YYYYY.parquet + splits: train, validation, test (clean, standardized) + common (11 corruptions, severity 3) + random_linf_30 (uniform noise, linf <= 30/255) + __ (transfer attacks, AutoAttack standard) + +Images are copied byte-for-byte from the source PNGs (no re-encoding), so +perturbations are preserved exactly. + +Usage: python build_parquet.py [dataset ...] +""" +import csv +import io +import json +import sys +import tarfile +from pathlib import Path + +import numpy as np +import zstandard as zstd +from datasets import ClassLabel, Dataset, Features, Image, Value +from PIL import Image as PILImage + +SRC = Path(__file__).parent / "src" +OUT = Path(__file__).parent / "out" / "data" +MAX_SHARD_BYTES = 400 * 1024**2 +ROWS_PER_GROUP = 100 # small row groups keep the Dataset Viewer responsive + +DATASETS = ["caltech101", "fgvc-aircraft-2013b", "flowers-102", + "oxford-iiit-pet", "stanford_cars", "uc-merced-land-use-dataset"] + +# Round-robin assignment used to build the released common-corruption images: +# image i receives COMMON_CORRUPTIONS[i % 11] (verified on the pixel data). +COMMON_CORRUPTIONS = ["shot_noise", "impulse_noise", "defocus_blur", "motion_blur", + "zoom_blur", "snow", "brightness", "contrast", + "elastic_transform", "pixelate", "jpeg_compression"] + +# Surrogates kept in v2.0 (those reported in the paper). +SURROGATES = { + # folder -> (short name, checkpoint as loaded in robust_training/vision_enabled_language_models/craft_adversarial.py) + "zeroshot_clip_vitb16_laion2b": ("clip_vitb16", "open_clip ViT-B-16 / laion2b_s34b_b88k"), + "zeroshot_clip_vith14_laion2b": ("clip_vith14", "open_clip ViT-H-14 / laion2b_s32b_b79k"), + "zeroshot_metaclip_vith14_fullcc2_5b": ("metaclip_vith14", "open_clip ViT-H-14-quickgelu / metaclip_fullcc"), + "zeroshot_siglip2_so400m_patch14_384": ("siglip2_so400m", "google/siglip2-so400m-patch14-384"), + "zeroshot_siglip2_base_patch16_224": ("siglip2_base", "google/siglip2-base-patch16-224"), +} +# threat folder -> (norm, epsilon as stored, display) +THREATS = { + "l1_eps75_autoattack_standard": ("l1", 75.0, "75"), + "l1_eps300_autoattack_standard": ("l1", 300.0, "300"), + "l2_eps2_autoattack_standard": ("l2", 2.0, "2"), + "l2_eps8_autoattack_standard": ("l2", 8.0, "8"), + "linf_eps4_autoattack_standard": ("linf", 4 / 255, "4"), + "linf_eps8_autoattack_standard": ("linf", 8 / 255, "8"), + "linf_eps30_autoattack_standard": ("linf", 30 / 255, "30"), +} +# SigLIP2 base is only reported at linf 30/255 in the paper. +KEEP_ONLY = {"siglip2_base": {"linf_eps30_autoattack_standard"}} + + +def read_archive(path, split): + with open(path, "rb") as f: + buf = io.BytesIO(zstd.ZstdDecompressor().stream_reader(f).read()) + tar = tarfile.open(fileobj=buf, mode="r:") + rows = list(csv.DictReader(io.TextIOWrapper(tar.extractfile(f"{split}/labels.csv")))) + return [(int(r["filename"].split(".")[0]), int(r["label"]), + tar.extractfile(f"{split}/{r['filename']}").read()) for r in rows] + + +def features(class_names): + return Features({ + "image": Image(), + "label": ClassLabel(names=class_names), + "image_id": Value("int32"), + "perturbation": Value("string"), + "surrogate": Value("string"), + "norm": Value("string"), + "epsilon": Value("float32"), + "corruption": Value("string"), + "is_modified": Value("bool"), + }) + + +def write_split(records, feats, out_dir, split): + out_dir.mkdir(parents=True, exist_ok=True) + total = sum(len(r["image"]["bytes"]) for r in records) + n_shards = max(1, -(-total // MAX_SHARD_BYTES)) + per = -(-len(records) // n_shards) + for s in range(n_shards): + chunk = records[s * per:(s + 1) * per] + ds = Dataset.from_list(chunk, features=feats) + ds.to_parquet(str(out_dir / f"{split}-{s:05d}-of-{n_shards:05d}.parquet"), + batch_size=ROWS_PER_GROUP) + return len(records), n_shards + + +def pixels(b): + return np.asarray(PILImage.open(io.BytesIO(b)).convert("RGB")) + + +def build(dataset): + class_names_map = json.loads((SRC / "class_names" / f"{dataset}.json").read_text()) + class_names = [class_names_map[str(i)] for i in range(len(class_names_map))] + feats = features(class_names) + out_dir = OUT / dataset + summary = {} + + clean = SRC / f"{dataset}_processed.tar.zst" + clean_test = None + for split, name in [("train", "train"), ("val", "validation"), ("test", "test")]: + items = read_archive(clean, split) + if split == "test": + clean_test = {i: (lab, pixels(b)) for i, lab, b in items} + recs = [dict(image={"bytes": b, "path": f"{i:05d}.png"}, label=lab, image_id=i, + perturbation="clean", surrogate=None, norm=None, epsilon=None, + corruption=None, is_modified=False) for i, lab, b in items] + summary[name] = write_split(recs, feats, out_dir, name) + + def perturbed(path, split_name, perturbation, surrogate, norm, eps, corr_fn=None): + items = read_archive(path, "test") + assert len(items) == len(clean_test), (path, len(items)) + recs = [] + for i, lab, b in items: + clab, cpix = clean_test[i] + assert lab == clab, f"label mismatch {path} image {i}" + recs.append(dict(image={"bytes": b, "path": f"{i:05d}.png"}, label=lab, image_id=i, + perturbation=perturbation, surrogate=surrogate, norm=norm, + epsilon=eps, corruption=corr_fn(i) if corr_fn else None, + is_modified=bool((pixels(b) != cpix).any()))) + summary[split_name] = write_split(recs, feats, out_dir, split_name) + + adv = SRC / "adversarial" + perturbed(adv / f"common/common_severity3/{dataset}__common_severity3_processed.tar.zst", + "common", "common_corruption_severity3", None, None, None, + corr_fn=lambda i: COMMON_CORRUPTIONS[i % len(COMMON_CORRUPTIONS)]) + perturbed(adv / f"random/linf_eps30_random_uniform/{dataset}__random__linf_eps30_random_uniform_processed.tar.zst", + "random_linf_30", "random_uniform", None, "linf", 30 / 255) + + for folder, (short, _hf_id) in SURROGATES.items(): + for threat, (norm, eps, disp) in THREATS.items(): + if short in KEEP_ONLY and threat not in KEEP_ONLY[short]: + continue + files = list((adv / folder / threat).glob(f"{dataset}__*.tar.zst")) + if not files: + continue + assert len(files) == 1, files + perturbed(files[0], f"{short}_{norm}_{disp}", "transfer_autoattack", + short, norm, float(eps)) + + (out_dir.parent.parent / "manifests").mkdir(parents=True, exist_ok=True) + (out_dir.parent.parent / "manifests" / f"{dataset}.json").write_text(json.dumps( + {"num_classes": len(class_names), "class_names": class_names, + "splits": {k: {"num_rows": v[0], "num_shards": v[1]} for k, v in summary.items()}}, + indent=1)) + print(dataset, {k: v[0] for k, v in summary.items()}) + + +if __name__ == "__main__": + for d in sys.argv[1:] or DATASETS: + build(d) diff --git a/scripts/make_card.py b/scripts/make_card.py new file mode 100644 index 0000000000000000000000000000000000000000..51536e71cea00fe0b54e827178d3620843cd891e --- /dev/null +++ b/scripts/make_card.py @@ -0,0 +1,230 @@ +"""Generate README.md (dataset card + viewer configs) from out/manifests/*.json.""" +import json +from pathlib import Path + +ROOT = Path(__file__).parent / "out" +DATASETS = [ # (config name, display, domain, granularity, upstream url, upstream license) + ("caltech101", "Caltech-101", "objects", "coarse", + "https://data.caltech.edu/records/mzrjq-6wc02", "CC BY 4.0"), + ("fgvc-aircraft-2013b", "FGVC-Aircraft (2013b)", "aircraft", "fine", + "https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/", "non-commercial research use only"), + ("flowers-102", "Oxford Flowers-102", "flowers", "fine", + "https://www.robots.ox.ac.uk/~vgg/data/flowers/102/", "see upstream terms"), + ("oxford-iiit-pet", "Oxford-IIIT Pet", "pets", "fine", + "https://www.robots.ox.ac.uk/~vgg/data/pets/", "CC BY-SA 4.0"), + ("stanford_cars", "Stanford Cars", "cars", "fine", + "https://ai.stanford.edu/~jkrause/cars/car_dataset.html", "see upstream terms (research use)"), + ("uc-merced-land-use-dataset", "UC Merced Land Use", "satellite", "coarse", + "http://weegee.vision.ucmerced.edu/datasets/landuse.html", "USGS public-domain imagery"), +] +SURR = [ # (prefix, display, 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`"), +] +THREATS = [("l1_75", "โ„“1", "75"), ("l1_300", "โ„“1", "300"), ("l2_2", "โ„“2", "2"), + ("l2_8", "โ„“2", "8"), ("linf_4", "โ„“โˆž", "4/255"), ("linf_8", "โ„“โˆž", "8/255"), + ("linf_30", "โ„“โˆž", "30/255")] +ORDER = ["train", "validation", "test", "common", "random_linf_30"] + \ + [f"{s}_{t}" for s, _, _ in SURR for t, _, _ in THREATS] + +man = {d[0]: json.loads((ROOT / "manifests" / f"{d[0]}.json").read_text()) for d in DATASETS} + +# โ”€โ”€ YAML header โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ +y = ["---", "license: cc-by-nc-4.0", "language:", "- en", "pretty_name: RobustGenBench", + "size_categories:", "- 100K__` | 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 | +|---|---|---| +{surr_rows} + +### 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`). + +{chr(10).join(cov)} + +## Data fields + +| Field | Type | Description | +|---|---|---| +| `image` | image | 224ร—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 | +|---|---|---| +{lic_rows} + +## 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). +""" +(ROOT / "README.md").write_text("\n".join(y) + "\n" + body) +print("README.md written,", len(y), "YAML lines") diff --git a/stanford_cars_processed.tar.zst b/stanford_cars_processed.tar.zst deleted file mode 100644 index 0068da17c0c233c6586fdaa0e26c9eb57e8af172..0000000000000000000000000000000000000000 --- a/stanford_cars_processed.tar.zst +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:931546370039475e9d40dfc711417dbdd2e5e793b4aa0b68b6786b0f58c32d72 -size 667199092 diff --git a/uc-merced-land-use-dataset_processed.tar.zst b/uc-merced-land-use-dataset_processed.tar.zst deleted file mode 100644 index 0ee3085a1b4ccae126876293f1c1d7f9e9dc6c45..0000000000000000000000000000000000000000 --- a/uc-merced-land-use-dataset_processed.tar.zst +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a7c9d2f39486dbe5708ec69a01df53bcf56ebf1ac73ea0798616b1bf9f5fff9e -size 184455683