About Machine Learning Model RetinaNet

RetinaNet is a state-of-the-art one-stage object detector, recognized for its performance comparable to classical two-stage approaches like Faster R-CNN, while being computationally more efficient. This model was introduced as part of a study focusing on a novel loss function named Focal Loss, designed to address the class imbalance problem in object detection tasks. The introduction of Focal Loss, which concentrates training on a sparse set of difficult examples and mitigates the overwhelming effect of numerous easy negatives, is a significant feature of RetinaNet. This approach was a breakthrough in object detection, allowing RetinaNet to achieve high accuracy more efficiently compared to prior methods.

Model Card

• Model Details:

  • Person or Organization Developing Model: Not explicitly mentioned in the available literature.
  • Model Date: The concept of RetinaNet was introduced around 2017.
  • Model Version: Original version (specific version not mentioned in the cited sources).
  • Model Type: One-stage object detector.
  • Training Algorithms, Parameters: Uses Focal Loss for addressing class imbalance.
  • Paper or Other Resource for More Information: Focal Loss for Dense Object Detection - arXiv.org
  • Citation Details: Not specified.
  • License: Not explicitly mentioned.

• Intended Use:

  • Primary Intended Uses: Object detection in images.
  • Primary Intended Users: Researchers and developers in computer vision.
  • Out-of-scope Use Cases: Specific out-of-scope uses not mentioned.

• Factors:

  • Relevant Factors: Not specified.
  • Evaluation Factors: Not specified.

• Metrics:

  • Model Performance Measures: Not explicitly detailed.
  • Decision Thresholds: Not specified.
  • Variation Approaches: Not specified.

• Evaluation Data:

  • Datasets: Not explicitly mentioned.
  • Motivation: To evaluate the effectiveness of Focal Loss.
  • Preprocessing: Not specified.

• Training Data:

  • Details: [More Information Needed].

• Quantitative Analyses:

  • Unitary Results: [More Information Needed].
  • Intersectional Results: [More Information Needed].

• Ethical Considerations:

  • [More Information Needed].

• Caveats and Recommendations:

  • [More Information Needed].

References

• Focal Loss for Dense Object Detection - arXiv.org
• RetinaNet Object Detector based on Analog-to-Spiking Neural Network - arXiv.org
• Light-Weight RetinaNet for Object Detection - arXiv.org