diff --git a/src/datasets.py b/src/datasets.py
index 12f79f0..1781529 100644
--- a/src/datasets.py
+++ b/src/datasets.py
@@ -143,12 +143,10 @@ class doublePhotonDataset(Dataset):
 
         ### random position for photons in 
         pos_x1 = np.random.randint(1, 4)
-        # pos_y1 = np.random.randint(1, 4)
-        pos_y1 = pos_x1
+        pos_y1 = np.random.randint(1, 4)
         sample[pos_y1:pos_y1+singlePhotonSize, pos_x1:pos_x1+singlePhotonSize] += photon1
         pos_x2 = np.random.randint(1, 4)
-        # pos_y2 = np.random.randint(1, 4)
-        pos_y2 = pos_x2
+        pos_y2 = np.random.randint(1, 4)
         sample[pos_y2:pos_y2+singlePhotonSize, pos_x2:pos_x2+singlePhotonSize] += photon2
         sample = sample[1:-1, 1:-1]  ### sample size: 6x6
         sample = torch.tensor(sample, dtype=torch.float32).unsqueeze(0)
@@ -160,5 +158,52 @@ class doublePhotonDataset(Dataset):
         label = np.concatenate((label1, label2), axis=0)
         return sample, torch.tensor(label, dtype=torch.float32)
         
+    def __len__(self):
+        return self.length
+
+
+class doublePhotonInferenceDataset(Dataset):
+    def __init__(self, sampleList, sampleRatio, datasetName):
+        self.sampleFileList = sampleList
+        self.sampleRatio = sampleRatio
+        self.datasetName = datasetName
+        self._init_coords()
+
+        all_samples = []
+        all_ref_pts = []
+        for idx, sampleFile in enumerate(self.sampleFileList):
+            if '.npz' in sampleFile:
+                data = np.load(sampleFile)
+                all_samples.append(data['samples'])
+                all_ref_pts.append(data['referencePoint'])
+            elif '.h5' in sampleFile:
+                import h5py
+                with h5py.File(sampleFile, 'r') as f:
+                    samples = f['clusters'][:]
+                    ref_pts = f['referencePoint'][:]
+                    all_samples.append(samples)
+                    all_ref_pts.append(ref_pts)
+        self.samples = np.concatenate(all_samples, axis=0) if all_samples else None
+        self.referencePoint = np.concatenate(all_ref_pts, axis=0) if all_ref_pts else None
+        ### total number of samples
+        self.length = int(self.samples.shape[0] * self.sampleRatio)
+        print(f"[{self.datasetName} dataset] \t Total number of samples: {self.length}")
+        
+    def _init_coords(self):
+        # Create a coordinate grid for 3x3 input
+        x = np.linspace(0, 5, 6)
+        y = np.linspace(0, 5, 6)    
+        x_grid, y_grid = np.meshgrid(x, y, indexing='ij')  # (6,6), (6,6)
+        self.x_grid = torch.tensor(np.expand_dims(x_grid, axis=0)).float().contiguous()  # (1, 6, 6)
+        self.y_grid = torch.tensor(np.expand_dims(y_grid, axis=0)).float().contiguous()  # (1, 6, 6)
+
+    def __getitem__(self, index):
+        sample = self.samples[index]
+        # sample[sample == 0] += np.random.normal(loc=0.0, scale=0.13, size=sample[sample == 0].shape)  ### add noise to zero pixels
+        sample = torch.tensor(sample, dtype=torch.float32).unsqueeze(0)
+        sample = torch.cat((sample, self.x_grid, self.y_grid), dim=0)  ### concatenate coordinate channels
+        dummy_label = np.zeros((8,), dtype=np.float32)  ### dummy label
+        return sample, torch.tensor(dummy_label, dtype=torch.float32)
+
     def __len__(self):
         return self.length
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