YOLOv11n Attention Module Performance on Tomato-Village Dataset

Zijian Chen published a performance comparison of different attention modules used with the YOLOv11n baseline model for object detection. The dataset, last updated on 2026-05-22, is a 9.5 KB Excel file containing evaluation results based on the Tomato-Village dataset.

Use Cases

• Benchmarking attention module performance for object detection based on the described comparison.
• Selecting optimal attention mechanisms for agricultural vision tasks based on the reported Tomato-Village dataset results.
• Reproducing or validating object detection model enhancements using the provided performance metrics.

Strengths

• Performance results are directly compared, with the best values bolded for clarity.
• Dataset is small (9.5 KB), facilitating quick download and inspection.

Limitations

• Row count and specific column definitions are unknown, limiting detailed assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Zijian Chen via figshare

Collection Method

Likely generated from experiments comparing attention modules with the YOLOv11n model.

Freshness

Last updated 2026-05-22 17:48:24; freshness should be verified.