diff --git a/Inference_SinglePhoton.py b/Inference_SinglePhoton.py
index 67cdf97..b610d7c 100644
--- a/Inference_SinglePhoton.py
+++ b/Inference_SinglePhoton.py
@@ -20,18 +20,47 @@ configs['SiemenStar'] = {
     'noise': 0.13  # in keV
 }
 BinningFactor = 10
+numberOfAugOps = 6
 Roi = configs['SiemenStar']['roi']
 X_st, X_ed, Y_st, Y_ed = Roi
 mlSuperFrame = np.zeros(((Y_ed-Y_st)*BinningFactor, (X_ed-X_st)*BinningFactor))
 countFrame = np.zeros((Y_ed-Y_st, X_ed-X_st))
 subpixelDistribution = np.zeros((BinningFactor, BinningFactor))
 
+def inv0(p): return p
+def inv1(p): return torch.stack([-p[..., 0], p[..., 1]], dim=-1)
+def inv2(p): return torch.stack([p[..., 0], -p[..., 1]], dim=-1)
+def inv3(p): return -p
+def inv4(p): return torch.stack([p[..., 1], p[..., 0]], dim=-1)
+def inv5(p): return torch.stack([p[..., 1], -p[..., 0]], dim=-1) 
+def inv6(p): return torch.stack([-p[..., 1], p[..., 0]], dim=-1)
+def inv7(p): return torch.stack([-p[..., 1], -p[..., 0]], dim=-1)
+
+INVERSE_TRANSFORMS = {
+    0: inv0,
+    1: inv1,
+    2: inv2,
+    3: inv3,
+    4: inv4,
+    5: inv5,
+    6: inv6,
+    7: inv7,
+}
+
+def apply_inverse_transforms(predictions: torch.Tensor, numberOfAugOps: int) -> torch.Tensor:
+    N = predictions.shape[0] // numberOfAugOps
+    preds = predictions.view(N, numberOfAugOps, 2)
+    corrected = torch.zeros_like(preds)
+    for idx in range(numberOfAugOps):
+        corrected[:, idx, :] = INVERSE_TRANSFORMS[idx](preds[:, idx, :])
+    return corrected.mean(dim=1)
+
 if __name__ == "__main__":
     task = 'SiemenStar'
     config = configs[task]
 
     model = models.get_model_class(config['modelVersion'])().cuda()
-    model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/singlePhoton{config["modelVersion"]}_15.3keV_Noise{config["noise"]}keV_E500.pth', weights_only=True))
+    model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/singlePhoton{config["modelVersion"]}_15.3keV_Noise{config["noise"]}keV_E500_aug8.pth', weights_only=True))
     predictions = []
     referencePoints = []
     nChunks = len(config['dataFiles']) // 32 + 1
@@ -40,12 +69,17 @@ if __name__ == "__main__":
         edFileIdx = min((idxChunk + 1) * 32, len(config['dataFiles']))
         sampleFiles = config['dataFiles'][stFileIdx : edFileIdx]
         print(f'Processing files {stFileIdx} to {edFileIdx}...')
-        dataset = singlePhotonDataset(sampleFiles, sampleRatio=1.0, datasetName='Inference')
+        dataset = singlePhotonDataset(
+            sampleFiles, 
+            sampleRatio=1.0, 
+            datasetName='Inference',
+            numberOfAugOps=numberOfAugOps
+            )
         dataLoader = torch.utils.data.DataLoader(
             dataset,
             batch_size=8192,
             shuffle=False,
-            num_workers=16,
+            num_workers=32,
             pin_memory=True,
         )
 
@@ -59,6 +93,7 @@ if __name__ == "__main__":
                 predictions.append(outputs.cpu())
 
     predictions = torch.cat(predictions, dim=0)
+    predictions = apply_inverse_transforms(predictions, numberOfAugOps)
     predictions += torch.tensor([1.5, 1.5]).unsqueeze(0)  # adjust back to original coordinate system
     referencePoints = np.concatenate(referencePoints, axis=0)
     print(f'mean x = {torch.mean(predictions[:, 0])}, std x = {torch.std(predictions[:, 0])}')