diff --git a/src/models.py b/src/models.py
index c1575a7..6cddaa2 100644
--- a/src/models.py
+++ b/src/models.py
@@ -254,6 +254,76 @@ class doublePhotonNet_251001_2(nn.Module):
         g_avg = self.global_avg_pool(c3).flatten(1)  # [B, 128]
         global_feat = torch.cat([g_max, g_avg], dim=1)  # [B, 256]
         
+        # Regression
+        coords = self.fc(global_feat)
+        return coords  # [B, 4]
+
+class doublePhotonNet_251124(nn.Module): ### adapted for 7x7 input from 251001_2
+    def __init__(self):
+        super().__init__()
+        # Backbone: deeper + residual-like blocks
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1) ### 7x7
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) ### 7x7
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1) ### 7x7
+        
+        # Spatial Attention Module (轻量但有效)
+        self.spatial_attn = nn.Sequential(
+            nn.Conv2d(128, 1, kernel_size=1),
+            nn.Sigmoid()
+        )
+        
+        # Multi-scale feature fusion (optional but helpful)
+        self.reduce1 = nn.Conv2d(32, 32, kernel_size=1)  # from conv1
+        self.reduce2 = nn.Conv2d(64, 32, kernel_size=1)  # from conv2
+        self.fuse = nn.Conv2d(32*3, 128, kernel_size=1)
+        
+        # Global context with both Max and Avg pooling (better than GAP alone)
+        self.global_max_pool = nn.AdaptiveMaxPool2d((1,1))
+        self.global_avg_pool = nn.AdaptiveAvgPool2d((1,1))
+        
+        # Enhanced regression head
+        self.fc = nn.Sequential(
+            nn.Linear(128 * 2, 256),   # concat max + avg
+            nn.ReLU(),
+            nn.Dropout(0.3),
+            nn.Linear(256, 128),
+            nn.ReLU(),
+            nn.Dropout(0.3),
+            nn.Linear(128, 4),
+            # nn.Sigmoid()  # output in [0,1]
+        )
+        
+        self._init_weights()
+    
+    def _init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_uniform_(m.weight)
+                nn.init.zeros_(m.bias)
+    
+    def forward(self, x):
+        # Feature extraction
+        c1 = F.relu(self.conv1(x))      # [B, 32, 7, 7]
+        c2 = F.relu(self.conv2(c1))     # [B, 64, 7, 7]
+        c3 = F.relu(self.conv3(c2))     # [B,128, 7, 7]
+        
+        # Spatial attention: highlight photon peaks
+        attn = self.spatial_attn(c3)    # [B, 1, 7, 7]
+        c3 = c3 * attn                  # reweight features
+        
+        # (Optional) Multi-scale fusion — uncomment if needed
+        # r1 = F.interpolate(self.reduce1(c1), size=(7,7), mode='nearest')
+        # r2 = self.reduce2(c2)
+        # fused = torch.cat([r1, r2, c3], dim=1)
+        # c3 = self.fuse(fused)
+        
+        # Global context: MaxPool better captures peaks, Avg for context
+        g_max = self.global_max_pool(c3).flatten(1)  # [B, 128]
+        g_avg = self.global_avg_pool(c3).flatten(1)  # [B, 128]
+        global_feat = torch.cat([g_max, g_avg], dim=1)  # [B, 256]
+        
         # Regression
         coords = self.fc(global_feat)
         return coords  # [B, 4]
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