diff --git a/Inference_SinglePhoton.py b/Inference_SinglePhoton.py
index 7cb6996..67cdf97 100644
--- a/Inference_SinglePhoton.py
+++ b/Inference_SinglePhoton.py
@@ -6,10 +6,16 @@ from datasets import *
 from tqdm import tqdm
 from matplotlib import pyplot as plt
 
+torch.manual_seed(42)
+torch.cuda.manual_seed(42)
+np.random.seed(42)
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+
 configs = {}
 configs['SiemenStar'] = {
-    'dataFiles': [f'/mnt/sls_det_storage/moench_data/MLXID/Samples/Measurement/2504_SOLEIL_SiemenStarClusters_MOENCH040_150V/clusters_chunk{i}.h5' for i in range(32)],
-    'modelVersion': '251020',
+    'dataFiles': [f'/mnt/sls_det_storage/moench_data/MLXID/Samples/Measurement/2504_SOLEIL_SiemenStarClusters_MOENCH040_150V/clusters_chunk{i}.h5' for i in range(200)],
+    'modelVersion': '251022',
     'roi': [140, 230, 120, 210], # x_min, x_max, y_min, y_max,
     'noise': 0.13  # in keV
 }
@@ -25,28 +31,36 @@ if __name__ == "__main__":
     config = configs[task]
 
     model = models.get_model_class(config['modelVersion'])().cuda()
-    model.load_state_dict(torch.load(f'/home/xie_x1/MLXID/DeepLearning/Models/singlePhotonNet_Noise{config["noise"]}keV_{config["modelVersion"]}.pth', weights_only=True))
-
-    dataset = singlePhotonDataset(config['dataFiles'], sampleRatio=1.0, datasetName='Inference')
-    dataLoader = torch.utils.data.DataLoader(
-        dataset,
-        batch_size=4096,
-        shuffle=False,
-        num_workers=16,
-        pin_memory=True,
-    )
-
-    referencePoints = dataset.referencePoint
+    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))
     predictions = []
+    referencePoints = []
+    nChunks = len(config['dataFiles']) // 32 + 1
+    for idxChunk in range(nChunks):
+        stFileIdx = idxChunk * 32
+        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')
+        dataLoader = torch.utils.data.DataLoader(
+            dataset,
+            batch_size=8192,
+            shuffle=False,
+            num_workers=16,
+            pin_memory=True,
+        )
 
-    with torch.no_grad():
-        for batch in tqdm(dataLoader):
-            inputs, _ = batch
-            inputs = inputs.cuda()
-            outputs = model(inputs)
-            predictions.append(outputs.cpu())
+        referencePoints.append(dataset.referencePoint)
+
+        with torch.no_grad():
+            for batch in tqdm(dataLoader):
+                inputs, _ = batch
+                inputs = inputs.cuda()
+                outputs = model(inputs)[:, :2]  # only x and y
+                predictions.append(outputs.cpu())
 
     predictions = torch.cat(predictions, dim=0)
+    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])}')
     print(f'mean y = {torch.mean(predictions[:, 1])}, std y = {torch.std(predictions[:, 1])}')
     absolutePositions = predictions.numpy() + referencePoints[:, :2] - 1