HugoHE/m-hood-dataset

M-Hood Dataset: Out-of-Distribution Evaluation Collection

This dataset collection contains out-of-distribution (OOD) image datasets specifically curated for evaluating the robustness of object detection models, particularly those trained to mitigate hallucination on out-of-distribution data.

🎯 Purpose

These datasets are designed to address limitations in existing OOD benchmarks and enable fine-grained analysis of hallucination suppression. They test how well object detection models perform when encountering images that differ from their training distribution. They are particularly useful for:

• Evaluating model robustness on out-of-distribution data
• Testing hallucination mitigation techniques
• Benchmarking domain adaptation capabilities
• Research on robust object detection

📊 Dataset Overview

Dataset	Images	Description	Domain
far-ood	1,000	Far out-of-distribution images with objects distinctly different from training domains, or backgrounds without recognizable objects.	General OOD
near-ood-bdd	1,000	Near OOD images related to BDD 100K driving domain, visually and semantically similar to training categories.	Autonomous Driving
near-ood-voc	1,000	Near OOD images related to Pascal VOC object classes, visually and semantically similar to training categories.	General Objects

📁 Dataset Structure

m-hood-dataset/
├── far-ood/
│   ├── 8a2b026a6c3d5ee2.jpg
│   ├── 5ec941c27b5a6c2f.jpg
│   └── ... (1,000 images)
├── near-ood-bdd/
│   ├── [image files]
│   └── ... (1,000 images)
└── near-ood-voc/
    ├── [image files]
    └── ... (1,000 images)

🔍 Dataset Details

These datasets were carefully sampled from over 500 diverse categories in OpenImagesV7 to provide challenging and reliable benchmarks for OOD detection in object detection models.

Far-OOD Dataset

• Images: 1,000 high-quality images
• Characteristics: Images contain objects distinctly different from typical object detection training domains, as well as backgrounds without recognizable objects. This dataset is designed for testing extreme out-of-distribution robustness.

Near-OOD-BDD Dataset

• Images: 1,000 high-quality images
• Domain: Related to autonomous driving (BDD 100K-adjacent)
• Characteristics: Images are visually and semantically similar to the training categories of autonomous driving datasets like BDD 100K, presenting a particularly challenging scenario for object detectors.
• Use Case: Testing domain shift robustness in autonomous driving scenarios.

Near-OOD-VOC Dataset

• Images: 1,000 high-quality images
• Domain: Related to Pascal VOC object classes
• Characteristics: Images are visually and semantically similar to the training categories of Pascal VOC, presenting a particularly challenging scenario for object detectors.
• Use Case: Testing domain shift robustness for general object detection.

🚀 Usage

Loading with Hugging Face Datasets

from datasets import load_dataset

# Load the entire dataset collection
dataset = load_dataset("HugoHE/m-hood-dataset")

# Access individual subsets
far_ood = dataset["far-ood"]
near_ood_bdd = dataset["near-ood-bdd"] 
near_ood_voc = dataset["near-ood-voc"]

Direct Download

You can also download specific subsets directly:

from huggingface_hub import snapshot_download

# Download specific dataset
snapshot_download(
    repo_id="HugoHE/m-hood-dataset",
    repo_type="dataset",
    local_dir="./datasets",
    allow_patterns="far-ood/*"  # or "near-ood-bdd/*" or "near-ood-voc/*"
)

Evaluation Example

from ultralytics import YOLO
import os
from PIL import Image

# Load your trained model
model = YOLO('path/to/your/model.pt')

# Evaluate on far-ood dataset
far_ood_dir = "path/to/far-ood"
results = []

for img_file in os.listdir(far_ood_dir):
    if img_file.endswith('.jpg'):
        img_path = os.path.join(far_ood_dir, img_file)
        result = model(img_path)
        results.append(result)

# Analyze results for hallucination/false positives

🔬 Research Applications

This dataset collection is particularly valuable for research in:

• Out-of-distribution detection
• Hallucination mitigation in object detection
• Domain adaptation and transfer learning
• Robust computer vision systems
• Autonomous driving perception robustness
• General object detection robustness

📈 Evaluation Metrics

When using these datasets for evaluation, consider these metrics:

• False Positive Rate (FPR): Rate of hallucinated detections
• Confidence Calibration: How well confidence scores reflect actual accuracy
• Detection Consistency: Consistency of detections across similar OOD images
• Domain Shift Sensitivity: Performance degradation compared to in-distribution data

🎯 Related Models

This dataset collection is designed to work with the M-Hood model collection available at:

• Repository: HugoHE/m-hood
• Models: YOLOv10 and Faster R-CNN variants trained on BDD 100K, Pascal VOC, and KITTI
• Fine-tuned variants: Specifically trained to mitigate hallucination on OOD data

📄 Citation

If you use this dataset collection in your research, please cite:

@dataset{mhood_ood_dataset,
  title={M-Hood Dataset: Out-of-Distribution Evaluation Collection for Object Detection},
  author={[Your Name]},
  year={2025},
  howpublished={\url{https://huggingface.co/datasets/HugoHE/m-hood-dataset}}
}

Note: These datasets were constructed using an automated data curation pipeline as part of the M-Hood project, originally described at https://gricad-gitlab.univ-grenoble-alpes.fr/dnn-safety/m-hood.

📜 License

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.