LeafBench is a visual question answering benchmark derived from the LeafNet dataset. It is designed to evaluate Vision-Language Models on six hierarchical diagnostic tasks for plant diseases. The dataset was created by author 'enalis' and was last updated on June 20, 2026.
Use Cases
- Benchmarking model performance on binary plant health screening tasks.
- Evaluating VLM capabilities for expert-level taxonomic reasoning about plant diseases.
- Training models for hierarchical diagnostic tasks based on visual and textual inputs.
Strengths
- Supports six hierarchical diagnostic tasks, providing a structured evaluation framework.
- Derived from the LeafNet dataset, which is described as a large-scale dataset.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last updated 2026-06-20 13:04:36; freshness should be verified.
Provenance
- Source
- huggingface
- Collection Method
- Derived from the LeafNet dataset.
- Freshness
- Last updated June 20, 2026.