2026-10-14T14:23:05Z https://www.scidb.cn/oai

10.57760/sciencedb.42719 2026-08-03T11:12:17Z

2026-08-03 Open-Set Recognition of Rice Leaf Diseases: CLIP vs. CNN (Code and Results) doi:10.57760/sciencedb.42719 en This dataset provides the code and pre-computed result files accompanying the paper "Open-Set Recognition of Rice Leaf Diseases: A Comparative Study of Vision-Language and Convolutional Architectures." It reproduces all experiments comparing a fine-tuned CLIP (ViT-B/32) against convolutional baselines (ResNet-50, EfficientNet-B0) on six rice leaf disease classes under a leave-one-class-out (LOCO) protocol. Included are the training/evaluation scripts, result files reporting five OSR metrics (AUROC, AUPR, FPR@TPR95, OSRC@0.5, OSRC@0.1) with multi-seed repeats and paired significance tests, and Figures 1–4. Image data are not redistributed; they were merged from two public sources (Mendeley Data, DOI 10.17632/fwcj7stb8r.1; and Baidu AI Studio, datasetdetail/254694) into 10,030 images across six classes, split 70/10/20 (seed 42). Experiments are CPU-based and seed-controlled; edit the paths in config.py before running. Code is released under the MIT License. open-set recognition; rice leaf disease; CLIP; vision-language model; convolutional neural network; plant disease Li Jiaming PUBLIC https://mit-license.org dataset Science Data Bank