| --- |
| 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) |
|
|
|
|
| [](https://arxiv.org/abs/2503.17071) |
| [](https://github.com/PAGF188/RAXO) |
| [](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}, |
| } |
| ``` |
