Super-squash branch 'main' using huggingface_hub

Co-authored-by: lorenzovaquero <lorenzovaquero@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
+  - split: invisible
+    path: data/invisible-*
+- config_name: raxo
+  data_files:
+  - split: test
+    path: raxo/test-*
+  - split: prototypes
+    path: raxo/prototypes-*
+dataset_info:
+- config_name: default
+  features:
+  - name: rgb_image
+    dtype: image
+  - name: rgb_width
+    dtype: int64
+  - name: rgb_height
+    dtype: int64
+  - name: xray_images
+    list: image
+  - name: xray_width
+    dtype: int64
+  - name: xray_height
+    dtype: int64
+  - name: objects
+    struct:
+    - name: rgb_bbox
+      list:
+        list: float64
+    - name: xray_bbox
+      list:
+        list: float64
+    - name: categories
+      list: string
+    - name: rgb_visible
+      list: bool
+    - name: xray_visible
+      list: bool
+    - name: dangerous
+      list: bool
+    - name: IN_ids
+      list: string
+    - name: WN_ids
+      list: string
+  splits:
+  - name: test
+    num_bytes: 4870749491.831
+    num_examples: 1573
+  - name: invisible
+    num_bytes: 1117400199
+    num_examples: 355
+  download_size: 5963943954
+  dataset_size: 5988149690.831
+- config_name: raxo
+  features:
+  - name: rgb_image
+    dtype: image
+  - name: rgb_width
+    dtype: int64
+  - name: rgb_height
+    dtype: int64
+  - name: xray_images
+    list: image
+  - name: xray_width
+    dtype: int64
+  - name: xray_height
+    dtype: int64
+  - name: objects
+    struct:
+    - name: rgb_bbox
+      list:
+        list: float64
+    - name: xray_bbox
+      list:
+        list: float64
+    - name: categories
+      list: string
+    - name: rgb_visible
+      list: bool
+    - name: xray_visible
+      list: bool
+    - name: dangerous
+      list: bool
+    - name: IN_ids
+      list: string
+    - name: WN_ids
+      list: string
+  splits:
+  - name: test
+    num_bytes: 966681256
+    num_examples: 307
+  - name: prototypes
+    num_bytes: 3786846541.402
+    num_examples: 1227
+  download_size: 4754476276
+  dataset_size: 4753527797.402
+task_categories:
+- object-detection
+language:
+- en
+tags:
+- x-ray
+- open-vocabulary
+- training-free
+- benchmark
+- xray
+- detection
+- imagenet
+- wordnet
+size_categories:
+- 1K<n<10K
+---
+
+
+
+<div align="center">
+
+<h1> <a style="color:white; font-weight:bold;" href="https://pagf188.github.io/RAXO/">Superpowering Open-Vocabulary Object Detectors for X-ray Vision</a></h1>
+<h2>ICCV 2025</h2>
+
+[Pablo Garcia-Fernandez](https://scholar.google.es/citations?user=xbtLSCcAAAAJ),
+[Lorenzo Vaquero](https://scholar.google.es/citations?user=G0ZcGDYAAAAJ),
+[Mingxuan Liu](https://scholar.google.com/citations?user=egL5-LsAAAAJ),
+[Feng Xue](https://scholar.google.com/citations?user=66SeiQsAAAAJ),
+[Daniel Cores](https://scholar.google.com/citations?user=pJqkUWgAAAAJ),
+[Nicu Sebe](https://scholar.google.com/citations?user=stFCYOAAAAAJ),
+[Manuel Mucientes](https://scholar.google.com/citations?user=raiz6p4AAAAJ),
+[Elisa Ricci](https://scholar.google.com/citations?user=xf1T870AAAAJ)
+
+
+[![arXiv](https://img.shields.io/badge/cs.CV-2410.07752-b31b1b?logo=arxiv&logoColor=red)](https://arxiv.org/abs/2503.17071)
+[![GitHub](https://img.shields.io/badge/GitHub-RAXO-blue?logo=github)](https://github.com/PAGF188/RAXO)
+[![Static Badge](https://img.shields.io/badge/website-RAXO-8A2BE2)](https://pagf188.github.io/RAXO/)
+</div>
+
+
+### DET-COMPASS
+
+This is the official repository of [Superpowering Open-Vocabulary Object Detectors for X-ray Vision](https://pagf188.github.io/RAXO/) (ICCV'25)
+
+<div align="center">
+  <img src="./figs/compass_qualitative.png" alt="Qualitative DET-COMPASS" width="60%">
+</div>
+
+
+### Dataset Summary
+
+Object detection in security X-ray scans has advanced significantly in recent years. However, evaluating Open-vocabulary Object Detectors (OvOD) detectors in this modality remains challenging due to the limited number of annotated object categories in existing X-ray benchmarks. This limitation severely constrains the comprehensive evaluation of OvOD methods, which require a broad and diverse category set to assess generalization to unseen object semantics. To address this gap, we introduce DET-COMPASS, a novel benchmark that repurposes the COMPASS-XP classification dataset for object detection through meticulous bounding box annotation. DET-COMPASS comprises 370 distinct object classes, offering an order-of-magnitude increase in vocabulary size over previous X-ray detection benchmarks. Additionally, it provides pixel-aligned RGB images, ensuring precise spatial correspondence across modalities and facilitating the development of multimodal models. Each object is also labeled with a visibility attribute, indicating whether it produces a discernible signature in the X-ray spectrum. 
+
+### Dataset Structure
+
+DET-COMPASS is provided in two configurations:
+
+- **default**: The entire dataset is used in a zero-shot setting, where only text labels are available for each object. This configuration is suitable for evaluating open-vocabulary object detectors without access to any visual prototypes.
+- **raxo**: This configuration replicates the setting in the RAXO paper, where a subset of x-ray prototypes for the objects is available. It is intended for experiments that leverage a small number of visual examples per class.
+
+Each sample in the dataset has the following structure:
+
+```json
+{
+  "rgb_image": PIL.Image,                        // loaded RGB image
+  "rgb_width": int,
+  "rgb_height": int,
+  "xray_images": [                               // list of 5 loaded images, order: Colour, Grey, High, Low, Density
+    PIL.Image, PIL.Image, PIL.Image, PIL.Image, PIL.Image
+  ],
+  "xray_width": int,
+  "xray_height": int,
+  "objects": {
+    "rgb_bbox": [ [float, float, float, float], ... ],   // list of [x_min, y_min, width, height]
+    "xray_bbox": [ [float, float, float, float], ... ],  // list of [x_min, y_min, width, height]
+    "categories": [ string, ... ],                       // list of text labels
+    "rgb_visible": [ bool, ... ],                        // list of booleans
+    "xray_visible": [ bool, ... ],                       // list of booleans
+    "dangerous": [ bool, ... ],                          // list of booleans
+    "IN_ids": [ string, ... ],                           // list of ImageNet IDs (may be empty)
+    "WN_ids": [ string, ... ]                            // list of WordNet synset IDs (may be empty)
+  }
+}
+```
+
+- The `xray_images` list always follows this order: **Colour, Grey, High, Low, Density**.
+- All bounding boxes are in `[x_min, y_min, width, height]` format.
+- The `categories` field contains the text label for each object.
+- The `IN_ids` and `WN_ids` fields provide ImageNet and WordNet synset IDs when available.
+- **All lists within the `objects` field are aligned by index:** for any index `i`, the elements `rgb_bbox[i]`, `xray_bbox[i]`, `categories[i]`, `rgb_visible[i]`, `xray_visible[i]`, `dangerous[i]`, `IN_ids[i]`, and `WN_ids[i]` all correspond to the same object instance in the image.
+
+**The repository also includes a `classes.csv` file listing all classes present in the dataset. This file has the following columns:**
+
+- `class`: Class name (string)
+- `IN_id`: ImageNet class ID (string, may be empty)
+- `WN_id`: WordNet synset ID (string, may be empty)
+- `dangerous`: Whether the class is considered dangerous (boolean)
+
+This structure supports both zero-shot and prototype-based open-vocabulary object detection experiments.
+
+### Usage
+
+```python
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+# 1. Load the dataset
+ds = load_dataset("PAGF/DET-COMPASS", name="default", split="test")
+
+# 2. Select a sample
+sample = ds[739]
+
+# 3. Get the images
+rgb_img = sample["rgb_image"]
+xray_colour_img = sample["xray_images"][0]
+xray_grey_img = sample["xray_images"][1]
+xray_high_img = sample["xray_images"][2]
+xray_low_img = sample["xray_images"][3]
+xray_density_img = sample["xray_images"][4]
+
+# 4. Get the bounding boxes ([x_min, y_min, width, height])
+rgb_bboxes = sample["objects"]["rgb_bbox"]
+xray_bboxes = sample["objects"]["xray_bbox"]
+
+# 5. Get the classes
+classes = sample["objects"]["categories"]
+
+# 6. Draw bounding boxes
+def plot_image(ax, img, bboxes, classes, name=""):
+    ax.imshow(img)
+    for bbox, cls in zip(bboxes, classes):
+        rect = patches.Rectangle(
+            (bbox[0], bbox[1]),
+            bbox[2], bbox[3],
+            linewidth=2, edgecolor='r', facecolor='none'
+        )
+        ax.add_patch(rect)
+        # Draw class name at the top-left corner of the bbox
+        ax.text(
+            bbox[0], bbox[1] - 5,  # Slightly above the bbox
+            cls,
+            color='yellow', fontsize=10, weight='bold',
+            bbox=dict(facecolor='black', alpha=0.5, edgecolor='none', pad=1)
+        )
+    ax.set_title(name)
+    ax.axis("off")
+
+fig, axs = plt.subplots(3, 2, figsize=(12, 6))
+
+plot_image(ax=axs[0,0], img=rgb_img, bboxes=rgb_bboxes, classes=classes, name="RGB")
+plot_image(ax=axs[0,1], img=xray_colour_img, bboxes=xray_bboxes, classes=classes, name="X-ray Colour")
+plot_image(ax=axs[1,0], img=xray_grey_img, bboxes=xray_bboxes, classes=classes, name="X-ray Grey")
+plot_image(ax=axs[1,1], img=xray_high_img, bboxes=xray_bboxes, classes=classes, name="X-ray High")
+plot_image(ax=axs[2,0], img=xray_low_img, bboxes=xray_bboxes, classes=classes, name="X-ray Low")
+plot_image(ax=axs[2,1], img=xray_density_img, bboxes=xray_bboxes, classes=classes, name="X-ray Density")
+
+plt.tight_layout()
+plt.show()
+```
+
+<div align="center">
+  <img src="./figs/side_by_side_bbox.png" alt="DET-COMPASS Sample" width="80%">
+</div>
+
+### Citation
+
+If you use DET-COMPASS in your research, please cite:
+
+```bibtex
+@inproceedings{garcia2025superpowering,
+      title={Superpowering Open-Vocabulary Object Detectors for X-ray Vision}, 
+      author={Pablo Garcia{-}Fernandez and Lorenzo Vaquero and Mingxuan Liu and Feng Xue and Daniel Cores and Nicu Sebe and Manuel Mucientes and Elisa Ricci},
+      booktitle={Int. Conf. Comput. Vis. ({ICCV})},
+      year={2025},
+}
+```

classes.csv

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+class,IN_id,WN_id,dangerous
+abacus,398,n02666196,0
+abaya,399,n02667093,0
+amplifier,,n02705944,0
+analog watch,,n02708433,0
+apron,411,n02730930,0
+baby monitor,,,0
+backpack,414,n02769748,0
+bag of sweets,,,0
+baking dish,,n03862984,0
+ballpoint,418,n02783161,0
+banana,954,n07753592,0
+band aid,419,n02786058,0
+baseball bat,,n02799175,1
+baseball cap,,n02799323,0
+bath towel,434,n02808304,0
+bathing cap,433,n02807133,0
+beanie,,n02816768,0
+beer bottle,440,n02823428,0
+beer glass,441,n02823750,0
+bell pepper,945,n07720875,0
+belt,,n02827606,0
+bib,443,n02834397,0
+bicycle helmet,,n03127747,0
+bikini,445,n02837789,0
+binder,446,n02840245,0
+binoculars,447,n02841315,0
+bird feeder,,n02843553,0
+biscuits,,n07635155,0
+blowtorch,,n02855390,1
+boardgame,,n00502415,0
+book,,n02870092,0
+book jacket,921,n07248320,0
+boot,,n02872752,0
+bow tie,457,n02883205,0
+bowl,,n02881193,0
+bowler hat,,n02881757,0
+box cutter,,,1
+bracelet,,n02887970,0
+brassiere,459,n02892767,0
+bread knife,,n02893941,1
+brush,,n02908217,0
+bumbag,,n03321563,0
+butternut squash,942,n07717556,0
+cable,,n02934168,0
+caliper,,n02939866,0
+camcorder,,n02942349,0
+camera,,n02942699,0
+can opener,473,n02951585,0
+candle,470,n02948072,0
+canned food,,n07572957,0
+capo,,,0
+cardigan,474,n02963159,0
+cards,,n02962545,0
+carving knife,,n02973904,1
+cassette,481,n02978881,0
+cassette player,482,n02979186,0
+cd drive,,n02988156,0
+cellular telephone,487,n02992529,0
+cereal,,n07702796,0
+chain,488,n02999410,0
+charger,,n03008976,0
+chewing gum,,n07599998,0
+chisel,,n03020692,1
+chocolate,,n07601999,0
+chocolate sauce,960,n07836838,0
+christmas stocking,496,n03026506,0
+cigarettes,,n03030663,0
+clarinet,,n03037709,0
+coat hanger,,n03057920,0
+cocktail shaker,503,n03062245,0
+coffee mug,504,n03063599,0
+coffeepot,505,n03063689,0
+colander,,n03066849,0
+comb,,n03074855,0
+combination lock,507,n03075370,0
+comic book,917,n06596364,0
+compact disc,,n03079230,0
+condoms,,n03088164,0
+corkscrew,512,n03109150,1
+cotton buds,,n04367746,0
+cotton wool,,n14870078,0
+cowboy hat,515,n03124170,0
+craft knife,,,1
+crayon,,n03128248,0
+crisps,,n07712559,0
+crossword puzzle,918,n06785654,0
+crowbar,,n03138344,1
+cucumber,943,n07718472,0
+dagger,,n03158885,1
+denture,,n03176386,0
+deodorant,,n03176594,0
+diaper,529,n03188531,0
+digital watch,531,n03197337,0
+dinner jacket,,n03201776,0
+dishrag,533,n03207743,0
+dressing gown,,n03237992,0
+dvd player,,,0
+e cigarette,,n04520962,0
+e liquid,,,0
+electric fan,545,n03271574,0
+electric toothbrush,,n03274265,0
+envelope,549,n03291819,0
+espresso maker,550,n03297495,0
+extension cord,,n03307037,0
+face powder,551,n03314780,0
+fascinator,,,0
+feather boa,552,n03325584,0
+first aid kit,,,0
+floss,,n03175843,0
+flute,558,n03372029,0
+fork,,n03383948,0
+french loaf,930,n07684084,0
+frisbee,,n03397947,0
+frying pan,567,n03400231,0
+fur coat,568,n03404251,0
+gaffer tape,,n03253516,0
+game console,,n00458890,0
+gameboy,,,0
+gas canister,,n14842226,1
+glove,,n03441112,0
+glue gun,,,0
+goggles,,n03443912,0
+hacksaw,,n03474779,1
+hair clippers,,,0
+hair gel,,n03794136,0
+hair spray,585,n03476991,0
+hairbrush,,n03475581,0
+hammer,587,n03481172,1
+hand blower,589,n03483316,0
+handkerchief,591,n03485794,0
+hard disc,592,n03492542,0
+harmonica,593,n03494278,0
+hatchet,596,n03498962,1
+headphones,,n03261776,0
+hearing aid,,n03506727,0
+high heel,,n04276808,0
+hook,600,n03532672,1
+hourglass,604,n03544143,0
+ipad,,n03985232,0
+ipod,605,n03584254,0
+iron,606,n03584829,0
+jean,608,n03594734,0
+jersey,610,n03595614,0
+jewellery box,,n02978055,0
+jigsaw puzzle,611,n03598930,0
+joystick,613,n03602883,0
+jumper,,n04370048,0
+kettle,,n03612814,0
+keys,,n03615037,0
+kimono,614,n03617480,0
+kindle,,,0
+kiwi,,n07763629,0
+knee pad,615,n03623198,0
+knife,,n03623556,1
+lab coat,617,n03630383,0
+ladle,618,n03633091,0
+lampshade,619,n03637318,0
+laptop,620,n03642806,0
+laser pointer,,n03643253,1
+leather jacket,,n14759722,0
+lemon,951,n07749582,0
+lens,,n02943241,0
+lens cap,622,n03657121,0
+letter opener,623,n03658185,1
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+lime,,n07749731,0
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+loudspeaker,632,n03691459,0
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+magazine,,n03704210,0
+magnetic compass,635,n03706229,0
+maillot 2,639,n03710721,0
+mallet,,n03715386,1
+marker,,n03722007,0
+mask,643,n03724870,0
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+measuring cup,647,n03733805,0
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+milk carton,,,0
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+pencil box,709,n03908618,0
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+pop bottle,737,n03983396,0
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