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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-*
+dataset_info:
+  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: 5996075853.088
+    num_examples: 1928
+  download_size: 5962600768
+  dataset_size: 5996075853.088
+---
+
+
+
+<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 25</h2>
+
+[Pablo Garcia-Fernandez](https://scholar.google.es/citations?user=xbtLSCcAAAAJ&hl=es),
+[Lorenzo Vaquero](https://scholar.google.es/citations?user=G0ZcGDYAAAAJ&hl=es&oi=sra),
+[Mingxuan Liu](https://scholar.google.com/citations?user=egL5-LsAAAAJ&hl=en),
+[Feng Xue](https://scholar.google.com/citations?user=66SeiQsAAAAJ&hl=zh-CN),
+[Daniel Cores](https://scholar.google.com/citations?user=pJqkUWgAAAAJ&hl=es)
+[Nicu Sebe](https://scholar.google.com/citations?user=stFCYOAAAAAJ&hl=en)
+[Manuel Mucientes](https://scholar.google.com/citations?user=raiz6p4AAAAJ)
+[Elisa Ricci](https://scholar.google.com/citations?user=xf1T870AAAAJ&hl=en)
+
+
+[![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 [RAXO](https://pagf188.github.io/RAXO/) (ICCV25)
+
+<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 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
+
+- 📁 **Low**: Raw 8-bit greyscale data from the scanner’s **low-energy X-ray channel**.
+- 📁 **High**: Raw 8-bit greyscale data from the scanner’s **high-energy X-ray channel**.
+- 📁 **Density**: 8-bit greyscale data representing **inferred material density**, computed by combining the low and high energy channels.
+- 📁 **Grey**: RGB images that blends low and high energy channels with **visual enhancements**. While nominally greyscale, it includes **subtle color details**.
+- 📁 **Colour**: RGB images using a **color palette** to represent material density variations.
+- 📁 **Photo**: RGB images caputred with a RGB camera.
+
+- 📄 **meta.txt**: Original metadata from COMPASS-XP, including:
+    ```
+  basename: Shared ID for each image pair
+  class: Object class
+  instance: An integer identifying the object instance
+  pose: An integer identifying the object pose
+  scan tray: Either A, indicating that the pose was imaged in a weighted tray, or N indicating it was not
+  dangerous: Boolean indicating aviation threat
+  IN_id: ImageNet class ID (if available)
+  WN_id: WordNet synset ID (if available)
+    ```
+- 📄 **rgb_compass.json** and 📄**xray_compass.json**: Bounding box annotations for both the X-ray (Low, High, Density, Grey, Colour), and the RGB (Photo) images.
+    > Note: The category id/name must be retrieved from `meta.txt` (now there is a dummy 1).
+- 📁 **Raxo_splits**: Train/Test splits used in the [RAXO](https://github.com/PAGF188/RAXO) paper, stored in **COCO format** .
+  - 📄 **xray_prot.json**: Used to build the prototypes.
+  - 📄 **xray_1image_test.json**: Used for test.
+  
+
+
+### Usage
+
+```
+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", split="test")
+
+# 2. Select a sample
+sample = ds[869]
+
+# 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()
+```
+
+### 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={Garcia-Fernandez, Pablo and Vaquero, Lorenzo and Liu, Mingxuan and Xue, Feng and Cores, Daniel and Sebe, Nicu and Mucientes, Manuel and Ricci, Elisa},
+      booktitle={ICCV},
+      year={2025},
+}
+```

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