Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium Orchids
Published: 4 September 2025| Version 2 | DOI: 10.17632/mxtvkt5dcy.2
Contributors:
Yingshu Peng, Yuxia Zhou, Li Zhang, Hongyan Fu, Guimei Tang, Guolin Huang, Weidong LiDescription
The authors dedicated over a year to collecting a cultivar image dataset for Chinese Cymbidium orchids named Orchid2024. This dataset contains over 150,000 images spanning 1,275 different categories, involving visits to 20 cities across 12 provincial administrative regions in China to gather pertinent data. Subsequently, we introduced various visual parameter-efficient fine-tuning (PEFT) methods to expedite model development, achieving the highest top-1 accuracy of 86.14% and top-5 accuracy of 95.44%.
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Institutions
Hunan Academy of Agricultural Sciences
Categories
Image Database, Image Classification, Flower, Orchid, Deep Learning
Funders
- Hunan Key Laboratory of Germplasm Innovation and Comprehensive Utilization of Ornamental PlantGrant ID: 2022YLHH001
- Hunan Provincial Science and Technology Innovation FundGrant ID: 2023CX95