BirDrone

The BirDrone dataset is compiled by aggregating images of small drones and birds sourced from various online datasets. It comprises 2970 high-resolution images (640x640 pixels), each featuring unique backdrops and lighting conditions. This dataset is designed to enhance machine learning models by simulating real-world scenarios.

Dataset Specifications:

• Image Count: 2970 images, with 2617 drone images and 353 bird images.
• Image Resolution: Each image is uniformly sized at 640x640 pixels.
• Annotation Details: The dataset includes 6162 annotations with the smallest bounding box sized at 7x14 pixels and the largest at 65x182 pixels.
• Classes: Two categories are represented—drones and birds.
• Annotation Format: Annotations are formatted according to YOLOv8 specifications.

Pre-processing and Augmentation:

• Pre-processing Techniques: Images have undergone auto-orientation, resizing, and auto-contrast adjustments to standardize and enhance visual clarity.
• Augmentation Techniques: To increase variability and robustness, the images have been augmented with rotation and exposure adjustments, preparing the dataset for diverse environmental and lighting conditions.

Dataset Distribution:

• Training Set: 80% (2376 images)
• Validation Set: 20% (594 images)

File Size: 96.3 MB