Update README.md

README.md

@@ -1,3 +1,80 @@
 ---
 license: apache-2.0
+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)
+```