diff --git a/1.py b/1.py
index cecbb2b..4c52375 100644
--- a/1.py
+++ b/1.py
@@ -1,14 +1,25 @@
 from ultralytics import YOLO,Explorer
 
 # Load a pre-trained YOLOv10n model
-# model = YOLO("./yolov10x.pt")
-explorer = Explorer(data="./dataset.yaml", model="yolov10x.pt")
-explorer.create_embeddings_table()
-similar_images_df = explorer.get_similar(img="./1.jpg")
-a=explorer.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%cake%' LIMIT 10")
-print(a)
-# model.train(data="./dataset.yaml", epochs=3)
+model = YOLO("./yolov10x.pt")
+# explorer = Explorer(data="./dataset.yaml", model="yolov10x.pt")
+# explorer.create_embeddings_table()
+# similar_images_df = explorer.get_similar(img="./1.jpg")
+# a=explorer.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%cake%' LIMIT 10")
+# print(a)
+results =model.train(data="./dataset.yaml", epochs=3)
+# results = model("./1.jpg")metrics = model.val()
+print(results)
 
+# 保存模型
+model.export(format='onnx')
+
+# results[0].show()
+# metrics = model.val()
+# print(metrics)
+
+# 保存模型
+# model.export(format='onnx')
 # Perform object detection on an image
 # results = model("./1.jpg")
 # print(results)
diff --git a/2.py b/2.py
new file mode 100644
index 0000000..4f85aad
--- /dev/null
+++ b/2.py
@@ -0,0 +1,82 @@
+import cv2
+import numpy as np
+import onnxruntime
+from PIL import Image
+
+# 函数用来调整图片尺寸，保持原始宽高比
+def letterbox_image(image, size):
+    '''根据指定大小调整图片尺寸，保持原图的长宽比例不变，多余部分填充'''
+    iw, ih = image.size  # 获取原图宽高
+    w, h = size  # 目标尺寸
+    scale = min(w/iw, h/ih)  # 计算缩放比例
+    nw = int(iw*scale)  # 缩放后的宽度
+    nh = int(ih*scale)  # 缩放后的高度
+    
+    image = image.resize((nw,nh), Image.BICUBIC)  # 使用双三次插值调整图片大小
+    new_image = Image.new('RGB', size, (128,128,128))  # 创建新的RGB图片
+    new_image.paste(image, ((w-nw)//2, (h-nh)//2))  # 将调整后的图片粘贴到新创建的图片上
+    return new_image
+
+# 函数用来预处理输入图像
+def preprocess_image(img_path, model_image_size=(640, 640)):
+    # 加载图片并转换为RGB格式
+    img = Image.open(img_path).convert('RGB')
+    # 调整图片尺寸，保持长宽比
+    img = letterbox_image(img, tuple(reversed(model_image_size)))
+    # 转换为numpy数组并归一化到0-1之间
+    image_data = np.array(img, dtype='float32') / 255
+    # 增加一个维度作为批次大小
+    image_data = np.expand_dims(image_data, 0)
+    return image_data
+
+# 函数用来处理模型输出
+def postprocess_output(output, conf_threshold=0.5):
+    # 处理模型输出结果
+    boxes = []  # 存储检测框
+    scores = []  # 存储置信度
+    class_ids = []  # 存储类别ID
+
+    # 假设模型输出的形式为[x_center, y_center, width, height, confidence, ...class probabilities...]
+    for detection in output[0][0]:  # 遍历每个检测结果
+        scores = detection[5:]  # 获取每个类别的概率
+        class_id = np.argmax(scores)  # 找到概率最高的类别ID
+        confidence = scores[class_id]  # 获取最高概率
+        if confidence > conf_threshold:  # 如果超过阈值，则保存该检测框
+            box = detection[:4] * [width, height, width, height]  # 调整框大小到原图尺寸
+            (x_center, y_center, width, height) = box
+            x = int(x_center - (width / 2))  # 计算左上角X坐标
+            y = int(y_center - (height / 2))  # 计算左上角Y坐标
+            boxes.append([x, y, int(width), int(height)])  # 添加到检测框列表
+            scores.append(float(confidence))  # 添加置信度到列表
+            class_ids.append(class_id)  # 添加类别ID到列表
+
+    return boxes, scores, class_ids
+
+if __name__ == "__main__":
+    # 加载ONNX模型
+    ort_session = onnxruntime.InferenceSession("./best.onnx")
+
+    # 预处理图像
+    img_path = "./1.jpg"  # 图片路径
+    image_data = preprocess_image(img_path)
+
+    # 进行推理计算
+    ort_inputs = {ort_session.get_inputs()[0].name: image_data}  # 模型输入
+    ort_outs = ort_session.run(None, ort_inputs)  # 模型推理
+
+    # 后处理推理结果
+    boxes, scores, class_ids = postprocess_output(ort_outs)
+print(class_ids)
+    # # 可选地在图像上画出检测框
+    # img = cv2.imread(img_path)
+    # height, width = img.shape[:2]  # 获取图片的高度和宽度
+
+    # for box, score, class_id in zip(boxes, scores, class_ids):
+    #     x, y, w, h = box
+    #     cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)  # 在图片上画框
+    #     cv2.putText(img, f"{score:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)  # 在图片上显示置信度
+
+    # # 显示处理后的图像
+    # cv2.imshow("Image", img)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/best.onnx b/best.onnx
new file mode 100644
index 0000000..5b208f0
Binary files /dev/null and b/best.onnx differ
diff --git a/dataset.yaml b/dataset.yaml
index 5e4102a..363634e 100644
--- a/dataset.yaml
+++ b/dataset.yaml
@@ -2,4 +2,5 @@ train: F:/PythonLib/yolo/demo2/dataset/train/images
 val: F:/PythonLib/yolo/demo2/dataset/val/images
 
 nc: 1  # number of classes
-names: ['cabbage']  # class names
+names: 
+  0: '白菜'
diff --git a/dataset/train/classes.txt b/dataset/train/classes.txt
new file mode 100644
index 0000000..9d05dce
--- /dev/null
+++ b/dataset/train/classes.txt
@@ -0,0 +1 @@
+白菜
\ No newline at end of file
diff --git a/detect.py b/detect.py
new file mode 100644
index 0000000..b3eefc9
--- /dev/null
+++ b/detect.py
@@ -0,0 +1,9 @@
+from ultralytics import YOLO,Explorer
+
+model = YOLO("./yolov10x.pt")
+
+results =model.train(data="./dataset.yaml", epochs=3)
+print(results)
+# 保存模型
+# model.export(format='onnx')
+
diff --git a/segment.py b/segment.py
new file mode 100644
index 0000000..e69de29