File size: 2,803 Bytes
bbab4a8 e2403b7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
license: cc-by-nc-sa-4.0
base_model:
- Ultralytics/YOLO11
---
# 水稻病害检测 (with YOLO11L)
## 模型简介
- 模型功能：支持多种水稻病害的检测，返回图像中的病害位置(bounding box)以及病害类别(class label)。
- 支持类别：{0: '水稻白叶枯病Bacterial_Leaf_Blight', 1: '水稻胡麻斑病Brown_Spot', 2: '健康水稻HealthyLeaf', 3: '稻瘟病Leaf_Blast', 4: '水稻叶鞘腐病Leaf_Scald', 5: '水稻窄褐斑病Narrow_Brown_Leaf_Spot', 6: '水稻穗颈瘟Neck_Blast', 7: '稻飞虱Rice_Hispa'}
- 训练数据：3,567张水稻病害图像及对应标注信息（[Rice Leaf Spot Disease Annotated Dataset](https://www.kaggle.com/datasets/hadiurrahmannabil/rice-leaf-spot-disease-annotated-dataset)），训练200epoch。
- 评测指标：测试集 {mAP50: 56.3, mAP50-95: 34.9}
## 模型使用（with Data-Juicer）
- 输出格式：
```
[{
"images": image_path1,
"objects": {
"ref": [class_label1, class_label2, ...],
"bbox": [bbox1, bbox2, ...]
}
},
...
]
```
- 可参考代码：
```python
import json
from data_juicer.core.data import NestedDataset as Dataset
from data_juicer.ops.mapper.image_detection_yolo_mapper import ImageDetectionYoloMapper
from data_juicer.utils.constant import Fields, MetaKeys
if __name__ == "__main__":
image_path1 = "test1.jpg"
image_path2 = "test2.jpg"
image_path3 = "test3.jpg"
source_list = [{
'images': [image_path1, image_path2, image_path3]
}]
class_names =['水稻白叶枯病Bacterial_Leaf_Blight', '水稻胡麻斑病Brown_Spot', '健康水稻HealthyLeaf', '稻瘟病Leaf_Blast', '水稻叶鞘腐病Leaf_Scald', '水稻窄褐斑病Narrow_Brown_Leaf_Spot', '水稻穗颈瘟Neck_Blast', '稻飞虱Rice_Hispa']
op = ImageDetectionYoloMapper(
imgsz=640, conf=0.05, iou=0.5, model_path='Path_to_YOLO11L-Rice-Disease-Detection.pt')
dataset = Dataset.from_list(source_list)
if Fields.meta not in dataset.features:
dataset = dataset.add_column(name=Fields.meta,
column=[{}] * dataset.num_rows)
dataset = dataset.map(op.process, num_proc=1, with_rank=True)
res_list = dataset.to_list()[0]
new_data = []
for temp_image_name, temp_bbox_lists, class_name_lists in zip(res_list["images"], res_list["__dj__meta__"]["__dj__bbox__"], res_list["__dj__meta__"]["__dj__class_label__"]):
temp_json = {}
temp_json["images"] = temp_image_name
temp_json["objects"] = {"ref": [], "bbox":temp_bbox_lists}
for temp_object_label in class_name_lists:
temp_json["objects"]["ref"].append(class_names[int(temp_object_label)])
new_data.append(temp_json)
with open("./output.json", "w") as f:
json.dump(new_data, f)
``` |