Experimental Environment Configurations for ECL-RTDETR Rice Disease Detection Model

5.5 KB of experimental configuration data supporting the ECL-RTDETR model, a computer vision system for detecting rice diseases. The dataset, created by Yaojun Zhang and last updated in June 2026, likely contains parameters and settings used to achieve reported performance gains of 0.7% [email protected] and 22.2 FPS speed increase over a baseline model.

Use Cases

• Reproducing the ECL-RTDETR model's experimental results based on the described configuration parameters.
• Benchmarking other lightweight vision models against the reported performance metrics (mAP, FPS, GFLOPs).
• Analyzing the impact of architectural components like EfficientViT, CARAFE, and LDConv on model efficiency and accuracy.
• Developing new agricultural disease detection systems informed by the described model enhancements.

Strengths

• Model performance is quantified with specific metrics: 0.7% [email protected] improvement, 22.2 FPS speed increase, and reductions of 81.8 GFLOPs and 22.12M parameters.
• The dataset is openly licensed under CC-BY-4.0, facilitating reuse and verification.
• The experimental setup is described in detail, including the use of a lightweight backbone, CARAFE upsampling, and LDConv.

Limitations

• The dataset is very small at 5.5 KB, suggesting it contains only configuration parameters, not the underlying image data or full model weights.
• Column-level documentation is absent; the specific variables and their semantics must be inferred after download.
• The geographic and temporal scope of the underlying training/evaluation image data is not specified, which may limit understanding of model applicability.

Provenance

Source

Yaojun Zhang via figshare.

Collection Method

Likely generated as part of the research and development process for the ECL-RTDETR model.

Freshness

Last updated 2026-06-03 17:54:29.