Update README.md

README.md

@@ -53,7 +53,7 @@ dataset_info:
     num_bytes: 4870749491.831
     num_examples: 1573
   - name: invisible
-    num_bytes: 1117400199.0
+    num_bytes: 1117400199
     num_examples: 355
   download_size: 5963943954
   dataset_size: 5988149690.831
@@ -93,13 +93,28 @@ dataset_info:
       list: string
   splits:
   - name: test
-    num_bytes: 966681256.0
+    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
 ---
 
 
@@ -107,16 +122,16 @@ dataset_info:
 <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>
+<h2>ICCV 2025</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)
+[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)
@@ -127,7 +142,7 @@ dataset_info:
 
 ### DET-COMPASS
 
-This is the official repository of [RAXO](https://pagf188.github.io/RAXO/) (ICCV25)
+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%">
@@ -136,7 +151,7 @@ This is the official repository of [RAXO](https://pagf188.github.io/RAXO/) (ICCV
 
 ### 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. 
+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
 
@@ -174,10 +189,10 @@ import matplotlib.pyplot as plt
 import matplotlib.patches as patches
 
 # 1. Load the dataset
-ds = load_dataset("PAGF/DET-COMPASS", split="test")
+ds = load_dataset("PAGF/DET-COMPASS", name="default", split="test")
 
 # 2. Select a sample
-sample = ds[869]
+sample = ds[739]
 
 # 3. Get the images
 rgb_img = sample["rgb_image"]
@@ -238,8 +253,8 @@ 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},
+      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},
 }
 ```