AWPF-ResNet18 Mushroom Classification Model Performance Metrics

AWPF-ResNet18, a modified ResNet18 model with an Adaptive Window Pyramid Fusion module, achieved a 2.5% accuracy improvement in edible mushroom classification tasks. The model's performance metrics, including macro precision, F1-score, and recall, are documented in an Excel file. Author Xinhai Zhao published the results on figshare in April 2026.

Use Cases

• Benchmarking image classification model performance based on reported accuracy, precision, F1-score, and recall metrics
• Evaluating the effectiveness of adaptive multi-scale feature fusion modules in computer vision tasks
• Comparing novel model architectures against baseline ResNet18 for mushroom identification
• Assessing technical approaches for safe identification and categorization of mushrooms

Strengths

• Performance metrics are quantified with specific percentage improvements (e.g., 2.5% accuracy increase)
• The dataset is associated with a published study describing a novel model architecture (AWPF-ResNet18)
• File is available under an open CC-BY-4.0 license

Limitations

• Dataset size is only 5.5 KB, indicating limited scope and likely a small set of summary metrics
• Row count and column-level documentation are absent; field semantics must be inferred after download
• The description focuses on the model methodology; the exact content of the data file is unspecified

Provenance

Source

figshare

Freshness

Last updated 2026-04-16 17:31:47