Bounding Box Regression Loss Function Comparisons for DPCNet

A 5.5 KB Excel file compares bounding box regression loss functions within the DPCNet architecture. The dataset, authored by Linfeng Jia and updated in March 2026, supports analysis of model performance metrics. Specific row and column counts are unknown.

Use Cases

• Compare performance metrics like mAP for different bounding box loss functions in DPCNet.
• Analyze the impact of loss functions on small object detection and tiny target scales.
• Benchmark DPCNet results against a YOLO11n baseline model.
• Evaluate loss functions for their effect on multiscale representations and spatial guidance.

Strengths

• Dataset is openly licensed under CC BY 4.0.
• Focuses on a specific, contemporary architecture (DPCNet) updated in 2026.
• Addresses a targeted research problem in bounding box regression for object detection.

Limitations

• Extremely small scale at 5.5 KB, indicating very limited data content.
• No sample data or column definitions are provided, obscuring the data structure.
• Tags suggest a narrow, technical scope focused on a single model's ablation study.

Provenance

Source

figshare

Collection Method

null

Time Range

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Freshness

Last updated March 2026.

Geography

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