diff --git a/2007_train.txt b/2007_train.txt
index 1cc3fe38d6a8cd853598fb4ab147e805422c89c1..64aceddff4636d1b5282a33582959eccd9712f5b 100644
--- a/2007_train.txt
+++ b/2007_train.txt
@@ -1,939 +1,939 @@
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diff --git a/2007_val.txt b/2007_val.txt
index 61ded1da85e1ffc43612984938d263471bdb0bd9..8d1360ff7f911307e590610eb7fd4d80027a0855 100644
--- a/2007_val.txt
+++ b/2007_val.txt
@@ -1,105 +1,105 @@
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+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000671.jpg 424,227,456,201,466,213,434,239,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000672.jpg 199,268,210,263,227,308,215,312,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000675.jpg 398,253,409,249,424,286,413,290,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000700.jpg 274,168,278,111,301,113,296,170,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000719.jpg 444,272,455,217,474,221,464,275,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000745.jpg 106,163,112,135,264,169,257,197,0 9,139,15,121,81,141,76,159,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000749.jpg 72,152,76,133,178,154,174,173,0 65,163,71,144,173,176,167,195,0 60,180,67,160,167,195,160,215,0 204,191,210,163,435,209,429,237,0 275,225,279,205,430,235,426,255,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000752.jpg 88,40,122,17,200,134,166,157,0 175,169,228,144,315,336,261,360,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000753.jpg 125,143,237,126,241,154,129,171,0 355,288,395,280,398,293,357,301,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000770.jpg 306,170,318,162,335,188,323,195,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000795.jpg 196,80,196,51,208,51,208,80,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000838.jpg 331,101,362,94,374,148,343,155,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000855.jpg 239,139,252,138,260,215,248,216,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000917.jpg 341,149,346,83,360,84,355,150,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000918.jpg 315,152,330,132,385,172,370,192,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000922.jpg 378,125,393,115,417,148,403,158,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000927.jpg 231,146,272,115,287,135,246,166,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000932.jpg 185,106,207,85,265,148,243,168,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000941.jpg 325,160,325,104,347,104,347,160,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000946.jpg 213,152,225,132,278,166,265,186,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000951.jpg 416,138,447,136,448,146,417,148,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000954.jpg 156,260,206,243,212,261,162,278,0 210,77,230,70,241,99,222,107,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000966.jpg 117,135,123,101,132,102,126,136,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000967.jpg 385,182,389,147,398,148,393,183,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000971.jpg 187,70,207,54,215,64,194,80,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000993.jpg 69,161,96,133,155,192,128,220,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000996.jpg 122,170,139,154,172,189,155,205,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001019.jpg 179,129,208,101,216,111,188,138,0 124,224,124,211,162,211,162,224,0 33,143,37,131,66,140,62,152,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001022.jpg 241,222,250,212,269,231,259,240,0 422,133,468,123,473,144,427,155,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001044.jpg 294,109,326,107,327,124,295,125,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001054.jpg 295,112,316,112,316,119,295,119,0 409,231,430,229,431,235,410,236,0 319,267,338,264,339,270,320,274,0 209,208,223,206,224,212,210,214,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001058.jpg 184,66,184,59,208,59,208,66,0 301,178,322,178,322,183,301,183,0 211,214,231,214,231,220,211,220,0 101,155,116,155,116,160,101,160,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001074.jpg 146,129,162,126,164,136,147,138,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001078.jpg 153,77,171,68,190,106,172,115,0 147,172,147,150,159,150,159,172,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001099.jpg 58,177,58,149,70,149,70,177,0 159,65,159,45,168,45,168,65,0 136,214,136,186,148,186,148,214,0 188,152,188,132,198,132,198,152,0 236,120,245,118,248,135,239,137,0 298,144,298,127,308,127,308,144,0 335,68,346,64,353,84,342,88,0 251,236,280,219,288,232,259,250,0 259,265,275,243,286,251,271,273,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001102.jpg 52,33,54,14,62,15,60,34,0 28,158,41,158,41,184,28,184,0 82,124,82,104,92,104,92,124,0 129,89,141,89,141,107,129,107,0 143,209,170,188,181,202,153,223,0 150,231,172,213,183,226,161,244,0 192,116,192,99,201,99,201,116,0 228,41,238,36,247,54,237,58,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001128.jpg 54,78,57,68,84,75,81,86,0 69,164,72,154,103,163,100,173,0 193,80,197,69,236,85,231,96,0 186,221,189,211,217,221,214,230,0 310,159,313,149,342,158,339,168,0 377,82,381,71,407,82,403,92,0 412,158,415,147,440,155,437,165,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001133.jpg 240,81,241,68,282,70,281,84,0 144,173,146,164,168,167,166,177,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001145.jpg 51,80,51,72,74,72,74,80,0 387,122,388,115,407,119,405,126,0
+D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001158.jpg 91,29,95,22,117,34,113,41,0 26,74,27,66,46,71,45,78,0 25,129,26,122,42,124,41,132,0 79,159,80,153,96,155,95,161,0 215,140,234,134,236,143,218,148,0
diff --git a/train.py b/train.py
index e8c1f212a2d7cb0041854d4d935ca6eaaf425567..6f23256b206623edd73d69bd0c5630cdb4b158dd 100644
--- a/train.py
+++ b/train.py
@@ -41,7 +41,7 @@ if __name__ == "__main__":
     #   Cuda    是否使用Cuda
     #           没有GPU可以设置成False
     #---------------------------------#
-    Cuda            = True
+    Cuda            = False
     #---------------------------------------------------------------------#
     #   distributed     用于指定是否使用单机多卡分布式运行
     #                   终端指令仅支持Ubuntu。CUDA_VISIBLE_DEVICES用于在Ubuntu下指定显卡。
diff --git a/utils/dataloader.py b/utils/dataloader.py
index e209ed7a19f01868ed75afc3ea8edc86a618b617..75b7da45b2dfbf51d9da129926332146fc3be532 100644
--- a/utils/dataloader.py
+++ b/utils/dataloader.py
@@ -76,7 +76,6 @@ class YoloDataset(Dataset):
             #---------------------------------------------------#
             # box[:, 2:4] = box[:, 2:4] - box[:, 0:2]
             # box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2
-            
             #---------------------------------------------------#
             #   调整顺序，符合训练的格式
             #   labels_out中序号为0的部分在collect时处理
@@ -105,6 +104,12 @@ class YoloDataset(Dataset):
         #   获得预测框
         #------------------------------#
         box     = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
+        #------------------------------#
+        #   将polygon转换为rbox
+        #------------------------------#
+        rbox    = np.zeros((box.shape[0], 6))
+        rbox[..., :5] = poly2rbox(box[..., :8], (h, w), use_pi=True)
+        rbox[..., 5]  = box[..., 8]
         image   = image.resize((w,h), Image.BICUBIC)
         image_data  = np.array(image, np.float32)
         
diff --git a/utils/utils_bbox.py b/utils/utils_bbox.py
index 636f288ac7f11a91bd19284a465df59754ce0372..abe11f1a19730fafb4230b7be36fa4f8dc9ecb1a 100644
--- a/utils/utils_bbox.py
+++ b/utils/utils_bbox.py
@@ -1,14 +1,15 @@
 import numpy as np
 import torch
+import math
 from torchvision.ops import nms
-# from utils.nms_rotated import obb_nms
+from utils.nms_rotated import obb_nms
 
 class DecodeBox():
     def __init__(self, anchors, num_classes, input_shape, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]]):
         super(DecodeBox, self).__init__()
         self.anchors        = anchors
         self.num_classes    = num_classes
-        self.bbox_attrs     = 5 + 1 + num_classes
+        self.bbox_attrs     = 6 + num_classes
         self.input_shape    = input_shape
         #-----------------------------------------------------------#
         #   13x13的特征层对应的anchor是[142, 110],[192, 243],[459, 401]
@@ -62,6 +63,10 @@ class DecodeBox():
             w = torch.sigmoid(prediction[..., 2]) 
             h = torch.sigmoid(prediction[..., 3]) 
             #-----------------------------------------------#
+            #   获取旋转角度
+            #-----------------------------------------------#
+            angle       = torch.sigmoid(prediction[..., 4])
+            #-----------------------------------------------#
             #   获得置信度，是否有物体
             #-----------------------------------------------#
             conf        = torch.sigmoid(prediction[..., 5])
@@ -105,17 +110,17 @@ class DecodeBox():
             pred_boxes[..., 1]  = y.data * 2. - 0.5 + grid_y
             pred_boxes[..., 2]  = (w.data * 2) ** 2 * anchor_w
             pred_boxes[..., 3]  = (h.data * 2) ** 2 * anchor_h
-
+            pred_theta          = (angle.data - 0.5) * math.pi
             #----------------------------------------------------------#
             #   将输出结果归一化成小数的形式
             #----------------------------------------------------------#
             _scale = torch.Tensor([input_width, input_height, input_width, input_height]).type(FloatTensor)
-            output = torch.cat((pred_boxes.view(batch_size, -1, 4) / _scale,
+            output = torch.cat((pred_boxes.view(batch_size, -1, 4) / _scale, pred_theta.view(batch_size, -1, 1),
                                 conf.view(batch_size, -1, 1), pred_cls.view(batch_size, -1, self.num_classes)), -1)
             outputs.append(output.data)
         return outputs
 
-    def yolo_correct_boxes(self, box_xy, box_wh, input_shape, image_shape, letterbox_image):
+    def yolo_correct_boxes(self, box_xy, box_wh, angle, input_shape, image_shape, letterbox_image):
         #-----------------------------------------------------------------#
         #   把y轴放前面是因为方便预测框和图像的宽高进行相乘
         #-----------------------------------------------------------------#
@@ -136,23 +141,16 @@ class DecodeBox():
             box_yx  = (box_yx - offset) * scale
             box_hw *= scale
 
-        box_mins    = box_yx - (box_hw / 2.)
-        box_maxes   = box_yx + (box_hw / 2.)
-        boxes  = np.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]], axis=-1)
-        boxes *= np.concatenate([image_shape, image_shape], axis=-1)
+        box_xy = box_yx[..., ::-1] * image_shape
+        box_wh = box_hw[..., ::-1] * image_shape
+
+        boxes  = np.concatenate([box_xy[..., 0:1], box_xy[..., 1:2], box_wh[..., 0:1], box_wh[..., 1:2], angle[..., 0:1] ], axis=-1)
         return boxes
 
     def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
         #----------------------------------------------------------#
-        #   将预测结果的格式转换成左上角右下角的格式。
         #   prediction  [batch_size, num_anchors, 85]
         #----------------------------------------------------------#
-        box_corner          = prediction.new(prediction.shape)
-        box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
-        box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
-        box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
-        box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
-        prediction[:, :, :4] = box_corner[:, :, :4]
 
         output = [None for _ in range(len(prediction))]
         for i, image_pred in enumerate(prediction):
@@ -161,13 +159,12 @@ class DecodeBox():
             #   class_conf  [num_anchors, 1]    种类置信度
             #   class_pred  [num_anchors, 1]    种类
             #----------------------------------------------------------#
-            class_conf, class_pred = torch.max(image_pred[:, 5:5 + num_classes], 1, keepdim=True)
+            class_conf, class_pred = torch.max(image_pred[:, 6:6 + num_classes], 1, keepdim=True)
 
             #----------------------------------------------------------#
             #   利用置信度进行第一轮筛选
             #----------------------------------------------------------#
-            conf_mask = (image_pred[:, 4] * class_conf[:, 0] >= conf_thres).squeeze()
-
+            conf_mask = (image_pred[:, 5] * class_conf[:, 0] >= conf_thres).squeeze()
             #----------------------------------------------------------#
             #   根据置信度进行预测结果的筛选
             #----------------------------------------------------------#
@@ -177,10 +174,10 @@ class DecodeBox():
             if not image_pred.size(0):
                 continue
             #-------------------------------------------------------------------------#
-            #   detections  [num_anchors, 7]
-            #   7的内容为：x1, y1, x2, y2, obj_conf, class_conf, class_pred
+            #   detections  [num_anchors, 8]
+            #   8的内容为：x, y, w, h, angle, obj_conf, class_conf, class_pred
             #-------------------------------------------------------------------------#
-            detections = torch.cat((image_pred[:, :5], class_conf.float(), class_pred.float()), 1)
+            detections = torch.cat((image_pred[:, :6], class_conf.float(), class_pred.float()), 1)
 
             #------------------------------------------#
             #   获得预测结果中包含的所有种类
@@ -201,9 +198,9 @@ class DecodeBox():
                 #   使用官方自带的非极大抑制会速度更快一些！
                 #   筛选出一定区域内，属于同一种类得分最大的框
                 #------------------------------------------#
-                keep = nms(
-                    detections_class[:, :4],
-                    detections_class[:, 4] * detections_class[:, 5],
+                _, keep = obb_nms(
+                    detections_class[:, :5],
+                    detections_class[:, 5] * detections_class[:, 6],
                     nms_thres
                 )
                 max_detections = detections_class[keep]
@@ -227,9 +224,9 @@ class DecodeBox():
                 output[i] = max_detections if output[i] is None else torch.cat((output[i], max_detections))
             
             if output[i] is not None:
-                output[i]           = output[i].cpu().numpy()
-                box_xy, box_wh      = (output[i][:, 0:2] + output[i][:, 2:4])/2, output[i][:, 2:4] - output[i][:, 0:2]
-                output[i][:, :4]    = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
+                output[i]             = output[i].cpu().numpy()
+                box_xy, box_wh, angle = output[i][:, 0:2], output[i][:, 2:4], output[i][:, 4:5]
+                output[i][:, :5]      = self.yolo_correct_boxes(box_xy, box_wh, angle, input_shape, image_shape, letterbox_image)
         return output
 
     def non_max_suppression_obb(self, prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
diff --git a/voc_annotation.py b/voc_annotation.py
index 6c5509b824edb51730becd3226bf1a78cf7276eb..f2d1d4568a662213b28dc1078073928d30ad7840 100644
--- a/voc_annotation.py
+++ b/voc_annotation.py
@@ -56,7 +56,10 @@ def convert_annotation(year, image_id, list_file):
             continue
         cls_id = classes.index(cls)
         xmlbox = obj.find('rotated_bndbox')
-        b = (int(float(xmlbox.find('rotated_bbox_cx').text)), int(float(xmlbox.find('rotated_bbox_cy').text)), int(float(xmlbox.find('rotated_bbox_w').text)), int(float(xmlbox.find('rotated_bbox_h').text)), int(float(xmlbox.find('rotated_bbox_theta').text)))
+        b = (int(float(xmlbox.find('x1').text)), int(float(xmlbox.find('y1').text)), \
+            int(float(xmlbox.find('x2').text)), int(float(xmlbox.find('y2').text)), \
+            int(float(xmlbox.find('x3').text)), int(float(xmlbox.find('y3').text)), \
+            int(float(xmlbox.find('x4').text)), int(float(xmlbox.find('y4').text)))
         list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))
         
         nums[classes.index(cls)] = nums[classes.index(cls)] + 1
diff --git a/yolo.py b/yolo.py
index 3183e49630409edd09d660f4405dc27d7011eac6..e739683e97874d816fc226a6db232cf45a0581b4 100644
--- a/yolo.py
+++ b/yolo.py
@@ -25,8 +25,8 @@ class YOLO(object):
         #   验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
         #   如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
         #--------------------------------------------------------------------------#
-        "model_path"        : 'model_data/yolov7_weights.pth',
-        "classes_path"      : 'model_data/coco_classes.txt',
+        "model_path"        : 'logs/best_epoch_weights.pth',
+        "classes_path"      : 'model_data/ssdd_classes.txt',
         #---------------------------------------------------------------------#
         #   anchors_path代表先验框对应的txt文件，一般不修改。
         #   anchors_mask用于帮助代码找到对应的先验框，一般不修改。
@@ -46,7 +46,7 @@ class YOLO(object):
         #---------------------------------------------------------------------#
         #   只有得分大于置信度的预测框会被保留下来
         #---------------------------------------------------------------------#
-        "confidence"        : 0.5,
+        "confidence"        : 0.05,
         #---------------------------------------------------------------------#
         #   非极大抑制所用到的nms_iou大小
         #---------------------------------------------------------------------#
@@ -60,7 +60,7 @@ class YOLO(object):
         #   是否使用Cuda
         #   没有GPU可以设置成False
         #-------------------------------#
-        "cuda"              : True,
+        "cuda"              : False,
     }
 
     @classmethod
@@ -148,7 +148,8 @@ class YOLO(object):
             #---------------------------------------------------------#
             #   将预测框进行堆叠，然后进行非极大抑制
             #---------------------------------------------------------#
-            results = self.bbox_util.non_max_suppression_obb(torch.cat(outputs, 1), self.confidence, self.nms_iou, classes=self.num_classes)
+            results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape, 
+                        image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
                                                     
             if results[0] is None: 
                 return image
@@ -179,12 +180,10 @@ class YOLO(object):
         #---------------------------------------------------------#
         for i, c in list(enumerate(top_label)):
             predicted_class = self.class_names[int(c)]
-            poly            = top_polys[i]
+            poly            = top_polys[i].astype(np.int32)
             score           = top_conf[i]
 
-            polygon_list = [(poly[0], poly[1]), (poly[2], poly[3]), \
-                    (poly[4], poly[5]), (poly[6], poly[7])]
-
+            polygon_list = list(poly)
             label = '{} {:.2f}'.format(predicted_class, score)
             draw = ImageDraw.Draw(image)
             label_size = draw.textsize(label, font)
@@ -193,7 +192,7 @@ class YOLO(object):
             
             text_origin = np.array([poly[0], poly[1]], np.int32)
 
-            draw.polygon(xy=polygon_list, fill=(0, 0, 0), outline=self.colors[i], width=label_size)
+            draw.polygon(xy=polygon_list, outline=self.colors[c])
             draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
             del draw