diff --git a/code/d2lzh_pytorch/utils.py b/code/d2lzh_pytorch/utils.py
index 0e1a4b32df585c6d876eae524a7384fd25bdec77..df56e1f4d23a7c9426d1b7fa2c5aafbb728ff635 100644
--- a/code/d2lzh_pytorch/utils.py
+++ b/code/d2lzh_pytorch/utils.py
@@ -1028,6 +1028,58 @@ def MultiBoxDetection(cls_prob, loc_pred, anchor, nms_threshold = 0.5):
 
 
 
+# ################################# 9.6 ############################
+class PikachuDetDataset(torch.utils.data.Dataset):
+    """皮卡丘检测数据集类"""
+    def __init__(self, data_dir, part, image_size=(256, 256)):
+        assert part in ["train", "val"]
+        self.image_size = image_size
+        self.image_dir = os.path.join(data_dir, part, "images")
+        
+        with open(os.path.join(data_dir, part, "label.json")) as f:
+            self.label = json.load(f)
+            
+        self.transform = torchvision.transforms.Compose([
+            # 将 PIL 图片转换成位于[0.0, 1.0]的floatTensor, shape (C x H x W)
+            torchvision.transforms.ToTensor()])
+            
+    def __len__(self):
+        return len(self.label)
+    
+    def __getitem__(self, index):
+        image_path = str(index + 1) + ".png"
+        
+        cls = self.label[image_path]["class"]
+        label = np.array([cls] + self.label[image_path]["loc"], 
+                         dtype="float32")[None, :]
+        
+        PIL_img = Image.open(os.path.join(self.image_dir, image_path)
+                            ).convert('RGB').resize(self.image_size)
+        img = self.transform(PIL_img)
+        
+        sample = {
+            "label": label, # shape: (1, 5) [class, xmin, ymin, xmax, ymax]
+            "image": img    # shape: (3, *image_size)
+        }
+        
+        return sample
+
+def load_data_pikachu(batch_size, edge_size=256, data_dir = '../../data/pikachu'):  
+    """edge_size：输出图像的宽和高"""
+    image_size = (edge_size, edge_size)
+    train_dataset = PikachuDetDataset(data_dir, 'train', image_size)
+    val_dataset = PikachuDetDataset(data_dir, 'val', image_size)
+    
+
+    train_iter = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, 
+                                             shuffle=True, num_workers=4)
+
+    val_iter = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size,
+                                           shuffle=False, num_workers=4)
+    return train_iter, val_iter
+
+
+
 
 # ############################# 10.7 ##########################
 def read_imdb(folder='train', data_root="/S1/CSCL/tangss/Datasets/aclImdb"):