AWPF-ResNet18 Hyperparameters for Mushroom Image Classification

Xinhai Zhao published experimental hyperparameters for the AWPF-ResNet18 model on figshare in April 2026. The dataset likely contains parameters used to train a ResNet18-based model with an Adaptive Window Pyramid Fusion module for classifying edible mushrooms. The model's performance metrics, including accuracy and macro precision, are reported in the description.

Use Cases

• Benchmarking deep learning model performance based on reported accuracy and macro precision metrics
• Replicating or fine-tuning the AWPF-ResNet18 architecture for mushroom classification
• Analyzing the impact of adaptive multi-scale feature fusion modules on model efficacy
• Comparing classification results against other state-of-the-art models mentioned in the study

Strengths

• Specific performance improvements are quantified: accuracy increased by 2.5%, macro precision by 7.5%
• The dataset is openly licensed under CC-BY-4.0
• File size is 5.5 KB, indicating a compact and focused set of parameters

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset's scope is limited to a single model's hyperparameters

Provenance

Source

figshare

Collection Method

Likely generated from experiments described in the associated study.

Freshness

Last updated 2026-04-16 17:31:44; freshness should be verified