diff --git a/Configs/infer_1photon.yaml b/Configs/infer_1photon.yaml
index ffce71f..e5ede71 100644
--- a/Configs/infer_1photon.yaml
+++ b/Configs/infer_1photon.yaml
@@ -31,6 +31,24 @@ data:
       NY: 101
       file_range: [0, 48] # [0, 48)
       roi: [0, 101, 0, 101]
+    KnifeEdge_2filters:
+      file_pattern: "2603MaxIV_Edge2Filters_12keV/1Photon_CS3_chunk{}.h5" ### 12 keV
+      NX: 101
+      NY: 101
+      file_range: [0, 16] # [0, 16)
+      roi: [0, 101, 0, 101]
+    FlatField_2filters:
+      file_pattern: "2603MaxIV_Flat2Filters_12keV/1Photon_CS3_chunk{}.h5" ### 12 keV
+      NX: 101
+      NY: 101
+      file_range: [0, 16] # [0, 16)
+      roi: [0, 101, 0, 101]
+    Uniformity_test:
+      file_pattern: "2603MaxIV_Flat2Filters_12keV/1Photon_CS3_chunk{}.h5" ### 12 keV
+      NX: 101
+      NY: 101
+      file_range: [0, 1]
+      roi: [0, 101, 0, 101]
   
   energy: 12  # keV
   normalize: false # for inference dataloaders
@@ -38,13 +56,15 @@ data:
   num_workers: 16
 
 model:
-  version: "251022"
   base_dir: "/home/xie_x1/MLXID/DeepLearning/Results/"
-  experiment_name: "260408_1ph_12keV_v251022_03"
-  name: "singlePhoton251022_12keV_Noise0.13keV_E150_aug1.pth"
-  noise_keV: 0.13
+  # experiment_name: "260408_1ph_12keV_v251022_03"
+  # name: "singlePhoton251022_12keV_Noise0.13keV_E150_aug1.pth"
+  # experiment_name: "260511_1ph_12keV_v251022_00"
+  # name: "singlePhoton251022_12keV_Noise0.13keV_E150_aug1.pth"
+  experiment_name: "260512_1ph_12keV_v260511_06"
+  name: "singlePhoton260511_12keV_Noise0.13keV_E300_aug1.pth"
 
 inference:
   binning_factor: 10
-  chunk_size: 16  # 
+  chunk_size: 16  #  
   num_aug_ops: 8  # TTA: test-time augmentation
diff --git a/Infer_1Photon.py b/Infer_1Photon.py
index 16b5f1a..93a694c 100644
--- a/Infer_1Photon.py
+++ b/Infer_1Photon.py
@@ -60,7 +60,6 @@ def get_files_list(conf):
     return files
 
 def run_inference(model, data_loader, conf):
-    model.eval()
     all_predictions = []
     all_reference_points = data_loader.dataset.referencePoint
     with torch.no_grad():
@@ -144,7 +143,7 @@ def save_results(ml_super_frame, count_frame, subpixel_dist,
     plt.close()
     np.save(output_dir / '1Photon_subpixel_Distribution.npy', subpixel_dist)
     rms, mean = np.std(subpixel_dist), np.mean(subpixel_dist)
-    print(f"[Plotting]: Sub-pixel distribution: RMS/Mean: {rms/mean:.4f}")
+    print(f"[Plotting]: Sub-pixel distribution: RMS/Mean: {rms/mean:.4f}, expected  value = {1/np.sqrt(mean):.4f} for uniform distribution")
     
     print(f"Results saved to: {output_dir}")
 
@@ -153,7 +152,8 @@ if __name__ == "__main__":
     output_dir = prepare_output_folder(conf)
 
     ### model loading
-    model = get_model_class(conf.model.version)().cuda()
+    model_version = conf.model.experiment_name.split('_v')[-1][:6]
+    model = get_model_class(model_version)().cuda()
     model.load_state_dict(torch.load(f'{conf.model.base_dir}/{conf.model.experiment_name}/Models/{conf.model.name}', weights_only=True))
     model.eval()