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--- |
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license: cc-by-4.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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- split: invisible |
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path: data/invisible-* |
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- config_name: raxo |
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data_files: |
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- split: test |
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path: raxo/test-* |
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- split: prototypes |
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path: raxo/prototypes-* |
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dataset_info: |
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- config_name: default |
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features: |
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- name: rgb_image |
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dtype: image |
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- name: rgb_width |
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dtype: int64 |
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- name: rgb_height |
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dtype: int64 |
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- name: xray_images |
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list: image |
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- name: xray_width |
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dtype: int64 |
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- name: xray_height |
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dtype: int64 |
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- name: objects |
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struct: |
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- name: rgb_bbox |
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list: |
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list: float64 |
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- name: xray_bbox |
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list: |
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list: float64 |
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- name: categories |
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list: string |
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- name: rgb_visible |
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list: bool |
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- name: xray_visible |
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list: bool |
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- name: dangerous |
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list: bool |
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- name: IN_ids |
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list: string |
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- name: WN_ids |
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list: string |
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splits: |
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- name: test |
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num_bytes: 4870749491.831 |
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num_examples: 1573 |
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- name: invisible |
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num_bytes: 1117400199 |
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num_examples: 355 |
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download_size: 5963943954 |
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dataset_size: 5988149690.831 |
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- config_name: raxo |
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features: |
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- name: rgb_image |
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dtype: image |
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- name: rgb_width |
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dtype: int64 |
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- name: rgb_height |
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dtype: int64 |
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- name: xray_images |
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list: image |
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- name: xray_width |
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dtype: int64 |
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- name: xray_height |
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dtype: int64 |
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- name: objects |
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struct: |
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- name: rgb_bbox |
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list: |
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list: float64 |
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- name: xray_bbox |
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list: |
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list: float64 |
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- name: categories |
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list: string |
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- name: rgb_visible |
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list: bool |
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- name: xray_visible |
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list: bool |
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- name: dangerous |
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list: bool |
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- name: IN_ids |
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list: string |
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- name: WN_ids |
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list: string |
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splits: |
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- name: test |
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num_bytes: 966681256 |
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num_examples: 307 |
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- name: prototypes |
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num_bytes: 3786846541.402 |
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num_examples: 1227 |
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download_size: 4754476276 |
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dataset_size: 4753527797.402 |
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task_categories: |
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- object-detection |
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language: |
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- en |
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tags: |
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- x-ray |
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- open-vocabulary |
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- training-free |
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- benchmark |
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- xray |
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- detection |
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- imagenet |
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- wordnet |
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size_categories: |
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- 1K<n<10K |
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--- |
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<div align="center"> |
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<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> |
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<h2>ICCV 2025</h2> |
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[Pablo Garcia-Fernandez](https://scholar.google.es/citations?user=xbtLSCcAAAAJ), |
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[Lorenzo Vaquero](https://scholar.google.es/citations?user=G0ZcGDYAAAAJ), |
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[Mingxuan Liu](https://scholar.google.com/citations?user=egL5-LsAAAAJ), |
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[Feng Xue](https://scholar.google.com/citations?user=66SeiQsAAAAJ), |
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[Daniel Cores](https://scholar.google.com/citations?user=pJqkUWgAAAAJ), |
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[Nicu Sebe](https://scholar.google.com/citations?user=stFCYOAAAAAJ), |
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[Manuel Mucientes](https://scholar.google.com/citations?user=raiz6p4AAAAJ), |
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[Elisa Ricci](https://scholar.google.com/citations?user=xf1T870AAAAJ) |
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[](https://arxiv.org/abs/2503.17071) |
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[](https://github.com/PAGF188/RAXO) |
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[](https://pagf188.github.io/RAXO/) |
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</div> |
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### DET-COMPASS |
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This is the official repository of [Superpowering Open-Vocabulary Object Detectors for X-ray Vision](https://pagf188.github.io/RAXO/) (ICCV'25) |
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<div align="center"> |
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<img src="./figs/compass_qualitative.png" alt="Qualitative DET-COMPASS" width="60%"> |
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</div> |
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### Dataset Summary |
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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. |
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### Dataset Structure |
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DET-COMPASS is provided in two configurations: |
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- **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. |
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- **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. |
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Each sample in the dataset has the following structure: |
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```json |
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{ |
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"rgb_image": PIL.Image, // loaded RGB image |
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"rgb_width": int, |
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"rgb_height": int, |
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"xray_images": [ // list of 5 loaded images, order: Colour, Grey, High, Low, Density |
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PIL.Image, PIL.Image, PIL.Image, PIL.Image, PIL.Image |
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], |
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"xray_width": int, |
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"xray_height": int, |
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"objects": { |
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"rgb_bbox": [ [float, float, float, float], ... ], // list of [x_min, y_min, width, height] |
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"xray_bbox": [ [float, float, float, float], ... ], // list of [x_min, y_min, width, height] |
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"categories": [ string, ... ], // list of text labels |
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"rgb_visible": [ bool, ... ], // list of booleans |
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"xray_visible": [ bool, ... ], // list of booleans |
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"dangerous": [ bool, ... ], // list of booleans |
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"IN_ids": [ string, ... ], // list of ImageNet IDs (may be empty) |
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"WN_ids": [ string, ... ] // list of WordNet synset IDs (may be empty) |
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} |
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} |
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``` |
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- The `xray_images` list always follows this order: **Colour, Grey, High, Low, Density**. |
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- All bounding boxes are in `[x_min, y_min, width, height]` format. |
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- The `categories` field contains the text label for each object. |
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- The `IN_ids` and `WN_ids` fields provide ImageNet and WordNet synset IDs when available. |
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- **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. |
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**The repository also includes a `classes.csv` file listing all classes present in the dataset. This file has the following columns:** |
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- `class`: Class name (string) |
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- `IN_id`: ImageNet class ID (string, may be empty) |
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- `WN_id`: WordNet synset ID (string, may be empty) |
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- `dangerous`: Whether the class is considered dangerous (boolean) |
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This structure supports both zero-shot and prototype-based open-vocabulary object detection experiments. |
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### Usage |
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```python |
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from datasets import load_dataset |
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import matplotlib.pyplot as plt |
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import matplotlib.patches as patches |
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# 1. Load the dataset |
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ds = load_dataset("PAGF/DET-COMPASS", name="default", split="test") |
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# 2. Select a sample |
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sample = ds[739] |
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# 3. Get the images |
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rgb_img = sample["rgb_image"] |
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xray_colour_img = sample["xray_images"][0] |
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xray_grey_img = sample["xray_images"][1] |
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xray_high_img = sample["xray_images"][2] |
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xray_low_img = sample["xray_images"][3] |
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xray_density_img = sample["xray_images"][4] |
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# 4. Get the bounding boxes ([x_min, y_min, width, height]) |
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rgb_bboxes = sample["objects"]["rgb_bbox"] |
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xray_bboxes = sample["objects"]["xray_bbox"] |
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# 5. Get the classes |
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classes = sample["objects"]["categories"] |
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# 6. Draw bounding boxes |
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def plot_image(ax, img, bboxes, classes, name=""): |
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ax.imshow(img) |
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for bbox, cls in zip(bboxes, classes): |
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rect = patches.Rectangle( |
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(bbox[0], bbox[1]), |
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bbox[2], bbox[3], |
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linewidth=2, edgecolor='r', facecolor='none' |
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) |
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ax.add_patch(rect) |
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# Draw class name at the top-left corner of the bbox |
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ax.text( |
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bbox[0], bbox[1] - 5, # Slightly above the bbox |
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cls, |
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color='yellow', fontsize=10, weight='bold', |
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bbox=dict(facecolor='black', alpha=0.5, edgecolor='none', pad=1) |
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) |
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ax.set_title(name) |
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ax.axis("off") |
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fig, axs = plt.subplots(3, 2, figsize=(12, 6)) |
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plot_image(ax=axs[0,0], img=rgb_img, bboxes=rgb_bboxes, classes=classes, name="RGB") |
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plot_image(ax=axs[0,1], img=xray_colour_img, bboxes=xray_bboxes, classes=classes, name="X-ray Colour") |
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plot_image(ax=axs[1,0], img=xray_grey_img, bboxes=xray_bboxes, classes=classes, name="X-ray Grey") |
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plot_image(ax=axs[1,1], img=xray_high_img, bboxes=xray_bboxes, classes=classes, name="X-ray High") |
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plot_image(ax=axs[2,0], img=xray_low_img, bboxes=xray_bboxes, classes=classes, name="X-ray Low") |
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plot_image(ax=axs[2,1], img=xray_density_img, bboxes=xray_bboxes, classes=classes, name="X-ray Density") |
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plt.tight_layout() |
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plt.show() |
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``` |
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<div align="center"> |
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<img src="./figs/side_by_side_bbox.png" alt="DET-COMPASS Sample" width="80%"> |
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</div> |
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### Citation |
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If you use DET-COMPASS in your research, please cite: |
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```bibtex |
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@inproceedings{garcia2025superpowering, |
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title={Superpowering Open-Vocabulary Object Detectors for X-ray Vision}, |
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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}, |
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booktitle={Int. Conf. Comput. Vis. ({ICCV})}, |
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year={2025}, |
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} |
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``` |
