--- 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

Superpowering Open-Vocabulary Object Detectors for X-ray Vision

ICCV 2025

[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/) ### DET-COMPASS This is the official repository of [Superpowering Open-Vocabulary Object Detectors for X-ray Vision](https://pagf188.github.io/RAXO/) (ICCV'25)
Qualitative DET-COMPASS
### 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() ```
DET-COMPASS Sample
### 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}, } ```