TriNet

The TriNet Dataset is a carefully curated collection designed for object detection and image classification tasks, particularly in military and non-military contexts. With 850 labeled images across three key classes, this dataset is ideal for developing and evaluating computer vision models focused on security, defense, and surveillance applications.

Key Features:

• Three Distinct Classes:
  • Military
  • Para-Military
  • Non-Military
• Comprehensive Subsets:
  • Train: Data for training machine learning models.
  • Test: Data for model evaluation.
  • Valid: Data for validating models during training.
• Structured Annotations: YOLO-formatted annotation files for accurate bounding boxes and class labels.

Applications:

• Military Classification Research: Use the dataset to build models for military object identification and classification.
• Security & Surveillance Systems: Enhance security systems with advanced object detection capabilities.
• YOLO-based Deep Learning Projects: Perfect for training models using the YOLO (You Only Look Once) framework for real-time object detection.

Source & Inspiration:

The TriNet Dataset draws inspiration from real-world defense and security applications, providing valuable resources for researchers and developers working on cutting-edge computer vision models.

This dataset is sourced from Kaggle.