Selected Hyperparameters for a Cocoa Pod Disease Classification Model

Hyperparameters used to train a ResNetV2-101 model with a Learnable Gated Fusion Convolutional Block Attention Module (LGF-CBAM). The dataset was authored by Henry Techie-Menson and last updated on 2026-04-30. It is a small 5.5 KB XLS file.

Use Cases

• Benchmarking attention mechanisms in CNNs based on the described LGF-CBAM architecture.
• Reproducing the high-accuracy cocoa disease classification results mentioned in the description.
• Analyzing the impact of trainable gating parameters on model robustness across datasets.

Strengths

• Hyperparameters are associated with a model achieving up to 98.95% accuracy on a specific cocoa disease dataset.
• The model demonstrated cross-dataset robustness with accuracies above 96% on three other datasets.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB size suggests a very limited scope, likely containing only configuration parameters.

Provenance

Source

Henry Techie-Menson via figshare

Collection Method

Likely extracted from the training process of a deep learning model for cocoa pod disease classification.

Freshness

Last updated 2026-04-30 17:51:57; freshness should be verified.