Update README.md

README.md

@@ -36,7 +36,7 @@ These datasets are designed to test how well object detection models perform whe
 | Dataset | Images | Size | Description | Domain |
 |---------|--------|------|-------------|---------|
 | **far-ood** | 1,000 | 278MB | Far out-of-distribution images significantly different from training domains | General OOD |
-| **near-ood-bdd** | 1,945 | 337MB | Near OOD images related to BDD 100K driving domain | Autonomous Driving |
+| **near-ood-bdd** | 1,010 | 337MB | Near OOD images related to BDD 100K driving domain | Autonomous Driving |
 | **near-ood-voc** | 1,020 | 318MB | Near OOD images related to Pascal VOC object classes | General Objects |
 
 ## 📁 Dataset Structure
@@ -49,7 +49,7 @@ m-hood-dataset/
 │   └── ... (1,000 images)
 ├── near-ood-bdd/
 │   ├── [image files]
-│   └── ... (1,945 images)
+│   └── ... (1,010 images)
 └── near-ood-voc/
     ├── [image files]
     └── ... (1,020 images)
@@ -64,7 +64,7 @@ m-hood-dataset/
 - **Use Case**: Testing extreme out-of-distribution robustness
 
 ### Near-OOD-BDD Dataset  
-- **Images**: 1,945 high-quality images
+- **Images**: 1,010 high-quality images
 - **Size**: 337MB
 - **Domain**: Related to autonomous driving (BDD 100K-adjacent)
 - **Characteristics**: Images similar to but distinct from BDD 100K training distribution
@@ -178,4 +178,4 @@ This dataset collection is released under the MIT License.
 
 ## 🏷️ Keywords
 
-Out-of-Distribution, OOD, Object Detection, Computer Vision, Robustness Evaluation, Hallucination Mitigation, BDD 100K, Pascal VOC, Domain Adaptation, Model Evaluation
+Out-of-Distribution, OOD, Object Detection, Computer Vision, Robustness Evaluation, Hallucination Mitigation, BDD 100K, Pascal VOC, Domain Adaptation, Model Evaluation.