🚀 印度药用植物图像检测项目
本项目可基于植物或叶片的图像检测印度药用植物的类型，为植物识别提供了高效准确的解决方案。
🚀 快速开始
如需了解更多详细信息，请查看 Kaggle代码。
✨ 主要特性
- 评估指标：使用准确率（accuracy）和F1分数（f1）进行模型评估。
- 基础模型：基于
google/vit-base-patch16-224-in21k模型构建。
| 属性 |
详情 |
| 模型类型 |
google/vit-base-patch16-224-in21k |
| 评估指标 |
准确率、F1分数 |
📄 许可证
本项目采用Apache-2.0许可证。
📚 详细文档
分类报告
Classification report:
precision recall f1-score support
Amla 1.0000 1.0000 1.0000 116
Curry 1.0000 1.0000 1.0000 115
Betel 0.9914 1.0000 0.9957 115
Bamboo 1.0000 1.0000 1.0000 116
Palak(Spinach) 1.0000 1.0000 1.0000 116
Coriender 1.0000 1.0000 1.0000 115
Ashoka 1.0000 1.0000 1.0000 115
Seethapala 1.0000 1.0000 1.0000 115
Lemon_grass 1.0000 1.0000 1.0000 116
Pappaya 1.0000 1.0000 1.0000 115
Curry_Leaf 1.0000 1.0000 1.0000 116
Lemon 1.0000 0.9913 0.9956 115
Nooni 1.0000 1.0000 1.0000 116
Henna 1.0000 1.0000 1.0000 116
Mango 1.0000 1.0000 1.0000 116
Doddpathre 1.0000 1.0000 1.0000 115
Amruta_Balli 1.0000 1.0000 1.0000 115
Betel_Nut 1.0000 1.0000 1.0000 116
Tulsi 0.9914 0.9914 0.9914 116
Pomegranate 1.0000 1.0000 1.0000 115
Castor 1.0000 1.0000 1.0000 116
Jackfruit 1.0000 1.0000 1.0000 116
Insulin 1.0000 1.0000 1.0000 116
Pepper 1.0000 1.0000 1.0000 116
Raktachandini 1.0000 1.0000 1.0000 116
Aloevera 1.0000 1.0000 1.0000 116
Jasmine 1.0000 1.0000 1.0000 116
Doddapatre 1.0000 1.0000 1.0000 115
Neem 1.0000 1.0000 1.0000 115
Geranium 1.0000 1.0000 1.0000 115
Rose 1.0000 1.0000 1.0000 115
Gauva 1.0000 1.0000 1.0000 116
Hibiscus 1.0000 1.0000 1.0000 116
Nithyapushpa 1.0000 1.0000 1.0000 116
Wood_sorel 1.0000 1.0000 1.0000 115
Tamarind 1.0000 1.0000 1.0000 116
Guava 1.0000 1.0000 1.0000 116
Bhrami 1.0000 1.0000 1.0000 115
Sapota 1.0000 1.0000 1.0000 116
Basale 1.0000 1.0000 1.0000 116
Avacado 1.0000 1.0000 1.0000 116
Ashwagandha 1.0000 1.0000 1.0000 116
Nagadali 0.9897 0.8348 0.9057 115
Arali 1.0000 1.0000 1.0000 115
Ekka 1.0000 1.0000 1.0000 116
Ganike 0.8582 0.9914 0.9200 116
Tulasi 0.9913 0.9913 0.9913 115
Honge 1.0000 1.0000 1.0000 115
Mint 1.0000 1.0000 1.0000 116
Catharanthus 1.0000 1.0000 1.0000 116
Papaya 1.0000 1.0000 1.0000 116
Brahmi 1.0000 1.0000 1.0000 116
accuracy 0.9962 6012
macro avg 0.9966 0.9962 0.9961 6012
weighted avg 0.9966 0.9962 0.9962 6012
示例图片
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