PlantaNet: Hyperparameter Search Space for Plant Disease Classification Models

Md. Sifat Haque Zidan published a hyperparameter search space dataset on figshare in April 2026. The dataset supports a study that benchmarks eight tiny vision transformer architectures and two custom convolutional neural networks for classifying 51 plant disease and healthy classes. The underlying image dataset comprises approximately 45,000 original images expanded through augmentation.

Use Cases

• Benchmarking model architectures for plant disease classification based on the described validation and test accuracy metrics.
• Optimizing hyperparameters for resource-constrained deployment scenarios based on the study's focus on computational efficiency.
• Analyzing model interpretability for image classification based on the mention of qualitative Grad-CAM and Grad-CAM++ analyses.
• Comparing the performance of tiny vision transformers against custom convolutional neural networks based on the described benchmark.

Strengths

• Dataset is associated with models achieving high reported accuracy, with PlantaNet reaching 99.66% test accuracy.
• The study explicitly focuses on computational efficiency, reporting model sizes as compact as 9.85 MB.
• License is clearly defined as CC-BY-4.0, facilitating open use.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 5.5 KB file size suggests the dataset contains only hyperparameter configurations or summary results, not the underlying image data.

Provenance

Source

Md. Sifat Haque Zidan

Collection Method

Likely generated from the hyperparameter optimization process described in the associated study.

Time Range

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Freshness

Last updated 2026-04-24 17:24:11; freshness should be verified.

Geography

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