预训练调整

demo.py

@@ -3,7 +3,7 @@ import numpy as np
 # model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
 # or
 # wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
-model = YOLOv10('ckpts/20250630/best_gift_v10n.pt')
+model = YOLOv10('ckpts/20250701/best_gift_v10n.pt')
 
 # result = model.predict('./data/bandage.jpg', save=True, imgsz=[224, 224], conf=0.1)
 result = model.predict('/home/lc/data_center/gift/trace_subimgs/predict_actual_test/test', save=True, imgsz=[224, 224], conf=0.1)

ultralytics/utils/txt2xml.py

@@ -0,0 +1,77 @@
+# # ## # # # # # # # # # #using: 将yolo txt label 转换成xml标签
+# #-*- coding: utf-8 -*-
+
+import cv2
+import os
+
+xml_head = '''<annotation>
+    <folder>VOC2007</folder>
+    <filename>{}</filename>.
+    <source>
+    </source>
+    <owner>
+        <flickrid>null</flickrid>
+        <name>null</name>
+    </owner>
+    <size>
+        <width>{}</width>
+        <height>{}</height>
+        <depth>{}</depth>
+    </size>
+    <segmented>0</segmented>
+    '''
+xml_obj = '''
+    <object>
+        <name>{}</name>
+        <pose>Rear</pose>
+        <truncated>0</truncated>
+        <difficult>0</difficult>
+        <bndbox>
+            <xmin>{}</xmin>
+            <ymin>{}</ymin>
+            <xmax>{}</xmax>
+            <ymax>{}</ymax>
+        </bndbox>
+    </object>
+    '''
+xml_end = '''
+</annotation>'''
+
+# labels = ['mouse','snake','lizard']  # label for datasets
+# labels = ['mouse','snake','lizard','fire','smoke','open','close','R_0','R_1','G_0','G_1','Y_0','Y_1','mask','person','hat','reflect']  #  需要训练的所有类，label for datasets
+# labels = ['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']
+labels = ['1', '2', '3', '4']
+cnt = 0
+txt_path = 'txt2xml/labels/'
+xml_path = 'txt2xml/xmls/'
+img_path = 'txt2xml/images/'
+os.makedirs(xml_path, exist_ok=True)
+for yolo_file in os.listdir(txt_path):
+    jpg = img_path + yolo_file.replace('.txt', '.png')  # image path
+    txt = txt_path + yolo_file  # yolo label txt path
+    xml = xml_path + yolo_file.replace('.txt', '.xml')  # xml save path
+    print(jpg)
+    obj = ''
+
+    img = cv2.imread(jpg)
+    img_h, img_w = img.shape[0], img.shape[1]
+    head = xml_head.format(str(jpg), str(img_w), str(img_h), str(3))
+    with open(txt, 'r') as f:
+        for line in f.readlines():
+            yolo_datas = line.strip().split(' ')
+            label = int(float(yolo_datas[0].strip()))
+            center_x = round(float(str(yolo_datas[1]).strip()) * img_w)
+            center_y = round(float(str(yolo_datas[2]).strip()) * img_h)
+            bbox_width = round(float(str(yolo_datas[3]).strip()) * img_w)
+            bbox_height = round(float(str(yolo_datas[4]).strip()) * img_h)
+
+            xmin = str(int(center_x - bbox_width / 2))
+            ymin = str(int(center_y - bbox_height / 2))
+            xmax = str(int(center_x + bbox_width / 2))
+            ymax = str(int(center_y + bbox_height / 2))
+
+            obj += xml_obj.format(labels[label], xmin, ymin, xmax, ymax)
+    with open(xml, 'w') as f_xml:
+        f_xml.write(head + obj + xml_end)
+    cnt += 1
+    print(cnt)