Datasets:
File size: 10,021 Bytes
8b53b83 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 |
---
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},
}
``` |