diff --git a/build/CMakeCache.txt b/build/CMakeCache.txt
index 406c555..0422112 100644
--- a/build/CMakeCache.txt
+++ b/build/CMakeCache.txt
@@ -201,8 +201,8 @@ CMAKE_STATIC_LINKER_FLAGS_RELWITHDEBINFO:STRING=
 //Path to a program.
 CMAKE_STRIP:FILEPATH=/usr/bin/strip
 
-//The CMake toolchain file
-CMAKE_TOOLCHAIN_FILE:FILEPATH=/media/mht/ADATA/repos/cpp_tracker/vcpkg/scripts/buildsystems/vcpkg.cmake
+//No help, variable specified on the command line.
+CMAKE_TOOLCHAIN_FILE:UNINITIALIZED=../vcpkg/scripts/buildsystems/vcpkg.cmake
 
 //If this value is on, makefiles will be generated without the
 // .SILENT directive, and all commands will be echoed to the console
diff --git a/build/CMakeFiles/Makefile2 b/build/CMakeFiles/Makefile2
index 20ac202..c21eabb 100644
--- a/build/CMakeFiles/Makefile2
+++ b/build/CMakeFiles/Makefile2
@@ -192,9 +192,9 @@ CMakeFiles/dimp_tracker.dir/clean:
 # Target rules for target CMakeFiles/tracking_demo.dir
 
 # All Build rule for target.
-CMakeFiles/tracking_demo.dir/all: CMakeFiles/bb_regressor.dir/all
 CMakeFiles/tracking_demo.dir/all: CMakeFiles/dimp_tracker.dir/all
 CMakeFiles/tracking_demo.dir/all: CMakeFiles/resnet.dir/all
+CMakeFiles/tracking_demo.dir/all: CMakeFiles/bb_regressor.dir/all
 CMakeFiles/tracking_demo.dir/all: CMakeFiles/classifier.dir/all
 	$(MAKE) $(MAKESILENT) -f CMakeFiles/tracking_demo.dir/build.make CMakeFiles/tracking_demo.dir/depend
 	$(MAKE) $(MAKESILENT) -f CMakeFiles/tracking_demo.dir/build.make CMakeFiles/tracking_demo.dir/build
@@ -221,9 +221,9 @@ CMakeFiles/tracking_demo.dir/clean:
 # Target rules for target CMakeFiles/test_models.dir
 
 # All Build rule for target.
-CMakeFiles/test_models.dir/all: CMakeFiles/bb_regressor.dir/all
 CMakeFiles/test_models.dir/all: CMakeFiles/dimp_tracker.dir/all
 CMakeFiles/test_models.dir/all: CMakeFiles/resnet.dir/all
+CMakeFiles/test_models.dir/all: CMakeFiles/bb_regressor.dir/all
 CMakeFiles/test_models.dir/all: CMakeFiles/classifier.dir/all
 	$(MAKE) $(MAKESILENT) -f CMakeFiles/test_models.dir/build.make CMakeFiles/test_models.dir/depend
 	$(MAKE) $(MAKESILENT) -f CMakeFiles/test_models.dir/build.make CMakeFiles/test_models.dir/build
diff --git a/cimp/resnet/resnet.cpp b/cimp/resnet/resnet.cpp
index c4a4b47..0b157c3 100644
--- a/cimp/resnet/resnet.cpp
+++ b/cimp/resnet/resnet.cpp
@@ -237,7 +237,11 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
         return std::find(_output_layers.begin(), _output_layers.end(), layer_name) != _output_layers.end();
     };
 
+    // Print input shape
+    if (x.size(0) > 0) std::cout << "[DEBUG] Input shape: " << x.sizes() << std::endl;
+
     x = conv1->forward(x);
+    if (x.size(0) > 0) torch::save(x[0].cpu(), "test/output/resnet_debug/sample_0_after_conv1.pt");
     if (should_output("debug_resnet_conv1_output_for_bn1_input")) {
         outputs["debug_resnet_conv1_output_for_bn1_input"] = x.clone(); 
     }
@@ -247,30 +251,27 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
     if (bn1) {
         x = bn1->forward(x);
     }
+    if (x.size(0) > 0) torch::save(x[0].cpu(), "test/output/resnet_debug/sample_0_after_bn1.pt");
     // End apply bn1
 
     if (should_output("bn1_output")) outputs["bn1_output"] = x; 
     
     x = relu->forward(x); 
-    if (should_output("relu1_output")) outputs["relu1_output"] = x; 
+    if (x.size(0) > 0) torch::save(x[0].cpu(), "test/output/resnet_debug/sample_0_after_relu1.pt");
     
     // Save conv1_output AFTER bn1 and relu (matching Python behavior)
     if (should_output("conv1_output")) outputs["conv1_output"] = x;
     
     torch::Tensor x_pre_layer1 = maxpool->forward(x);
-    if (should_output("maxpool_output")) outputs["maxpool_output"] = x_pre_layer1;
+    if (x_pre_layer1.size(0) > 0) torch::save(x_pre_layer1[0].cpu(), "test/output/resnet_debug/sample_0_after_maxpool.pt");
 
     // Save output of layer1.0 block if requested
     if (should_output("layer1_0_block_output")) {
         if (layer1 && !layer1->is_empty()) {
             try {
-                // Get the base module pointer
                 std::shared_ptr<torch::nn::Module> base_module_ptr = layer1->ptr(0);
-                // Try to cast it to our BottleneckImpl (which is a torch::nn::Module)
                 auto bottleneck_impl_ptr = std::dynamic_pointer_cast<cimp::resnet::BottleneckImpl>(base_module_ptr);
-                
                 if (bottleneck_impl_ptr) {
-                    // Now call forward on the BottleneckImpl instance
                     outputs["layer1_0_block_output"] = bottleneck_impl_ptr->forward(x_pre_layer1); 
                 } else {
                     std::cerr << "ERROR: layer1->ptr(0) could not be dynamically cast to BottleneckImpl! Module type: " 
@@ -283,6 +284,7 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
     }
 
     torch::Tensor x_after_layer1 = layer1->forward(x_pre_layer1); 
+    if (x_after_layer1.size(0) > 0) torch::save(x_after_layer1[0].cpu(), "test/output/resnet_debug/sample_0_after_layer1.pt");
     if (should_output("layer1")) outputs["layer1"] = x_after_layer1;
 
     if (should_output("layer1_0_shortcut_output")) {
@@ -290,7 +292,6 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
             try {
                 std::shared_ptr<torch::nn::Module> first_block_module_ptr = layer1->ptr(0);
                 auto bottleneck_module_holder = std::dynamic_pointer_cast<cimp::resnet::BottleneckImpl>(first_block_module_ptr);
-                
                 if (bottleneck_module_holder) { 
                     if (bottleneck_module_holder->projection_shortcut) {
                         torch::Tensor shortcut_out = bottleneck_module_holder->projection_shortcut->forward(x_pre_layer1); 
@@ -306,12 +307,15 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
     torch::Tensor x_current = x_after_layer1; 
 
     x_current = layer2->forward(x_current); 
+    if (x_current.size(0) > 0) torch::save(x_current[0].cpu(), "test/output/resnet_debug/sample_0_after_layer2.pt");
     if (should_output("layer2")) outputs["layer2"] = x_current;
 
     x_current = layer3->forward(x_current); 
+    if (x_current.size(0) > 0) torch::save(x_current[0].cpu(), "test/output/resnet_debug/sample_0_after_layer3.pt");
     if (should_output("layer3")) outputs["layer3"] = x_current;
 
     x_current = layer4->forward(x_current); 
+    if (x_current.size(0) > 0) torch::save(x_current[0].cpu(), "test/output/resnet_debug/sample_0_after_layer4.pt");
     if (should_output("layer4")) outputs["layer4"] = x_current;
     
     if (should_output("features")) outputs["features"] = x_current;
diff --git a/compare_preprocessed_inputs_debug.py b/compare_preprocessed_inputs_debug.py
index 6d32a15..df18c40 100644
--- a/compare_preprocessed_inputs_debug.py
+++ b/compare_preprocessed_inputs_debug.py
@@ -14,8 +14,8 @@ def get_tensor(x):
             pass
     raise RuntimeError('Could not extract tensor')
 
-a = torch.load('test/output/resnet/sample_0_image_preprocessed_python.pt', weights_only=False)
-b = torch.load('test/output/resnet/sample_0_image_preprocessed_cpp.pt', weights_only=False)
+a = torch.load('test/output/resnet/sample_0_image_preprocessed_python.pt')
+b = torch.load('test/output/resnet/sample_0_image_preprocessed_cpp.pt')
 a = get_tensor(a)
 b = get_tensor(b)
 print('py:', a.shape, a.dtype, a.min().item(), a.max().item(), a.mean().item())
diff --git a/compare_resnet_outputs_debug.py b/compare_resnet_outputs_debug.py
index fb598ca..23dac96 100644
--- a/compare_resnet_outputs_debug.py
+++ b/compare_resnet_outputs_debug.py
@@ -1,42 +1,64 @@
 import torch
+import os
+from ltr.models.backbone import resnet50
 
-def get_tensor(x):
-    if isinstance(x, torch.Tensor):
-        return x
-    if hasattr(x, 'parameters') and len(list(x.parameters())) > 0:
-        return list(x.parameters())[0]
-    if callable(x):
-        try:
-            out = x()
-            if isinstance(out, torch.Tensor):
-                return out
-        except Exception:
-            pass
-    raise RuntimeError('Could not extract tensor')
+def save_tensor(tensor, path):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    torch.save(tensor.cpu(), path)
 
-def compare_tensors(py_path, cpp_path, label):
-    a = torch.load(py_path)
-    b = torch.load(cpp_path, weights_only=False)
-    b = get_tensor(b)
-    print(f'--- {label} ---')
-    print('py:', a.shape, a.dtype, a.min().item(), a.max().item(), a.mean().item())
-    print('cpp:', b.shape, b.dtype, b.min().item(), b.max().item(), b.mean().item())
-    print('diff:', (a-b).abs().max().item(), (a-b).abs().mean().item())
+# Load the preprocessed input tensor exported from C++
+preprocessed_input_path = 'test/input_samples/common/sample_0_image.pt'
+input_obj = torch.load(preprocessed_input_path, map_location='cpu', weights_only=False)
+if isinstance(input_obj, torch.Tensor):
+    input_tensor = input_obj
+else:
+    # Try to extract tensor from JIT module
+    if hasattr(input_obj, 'named_parameters'):
+        params = list(input_obj.named_parameters())
+        if params:
+            input_tensor = params[0][1]
+        else:
+            raise RuntimeError('No tensor found in JIT module')
+    else:
+        raise RuntimeError('Unknown input object type')
 
-compare_tensors('test/output_py/resnet_py/sample_0/conv1_output.pt', 'test/output/resnet/sample_0_conv1_output.pt', 'conv1_output')
-compare_tensors('test/output_py/resnet_py/sample_0/debug_resnet_conv1_output_for_bn1_input.pt', 'test/output/resnet/sample_0_debug_resnet_conv1_output_for_bn1_input.pt', 'debug_resnet_conv1_output_for_bn1_input')
-compare_tensors('test/output_py/resnet_py/sample_0/bn1_output.pt', 'test/output/resnet/sample_0_bn1_output.pt', 'bn1_output')
+# Add batch dimension if missing
+if input_tensor.dim() == 3:
+    input_tensor = input_tensor.unsqueeze(0)
 
-# Print BN1 epsilon and momentum from Python model
-try:
-    from ltr.models.backbone import resnet50
-    model = resnet50(output_layers=['layer1', 'layer2', 'layer3', 'layer4'], pretrained=False)
-    model.load_state_dict(torch.load('backbone_pure_tensors/state_dict.pt'))
-    print('\nPython BN1 epsilon:', model.bn1.eps)
-    print('Python BN1 momentum:', model.bn1.momentum)
-except Exception as e:
-    print('Could not print Python BN1 eps/momentum:', e)
+# Load Python ResNet model
+model = resnet50(output_layers=['layer1', 'layer2', 'layer3', 'layer4'], pretrained=False)
+# Optionally load weights if needed (uncomment and set path if required)
+# model.load_state_dict(torch.load('backbone_pure_tensors/state_dict.pt'))
+model.eval()
 
-# Print expected C++ BN1 epsilon and momentum (from code)
-print('C++ BN1 epsilon: 1e-5 (from C++ code)')
-print('C++ BN1 momentum: 0.1 (from C++ code)') 
\ No newline at end of file
+# Forward pass and save intermediate outputs
+x = input_tensor
+out_dir = 'test/output_py/resnet_debug/'
+
+# After conv1
+x1 = model.conv1(x)
+save_tensor(x1[0], os.path.join(out_dir, 'sample_0_after_conv1.pt'))
+# After bn1
+x2 = model.bn1(x1)
+save_tensor(x2[0], os.path.join(out_dir, 'sample_0_after_bn1.pt'))
+# After relu1
+x3 = model.relu(x2)
+save_tensor(x3[0], os.path.join(out_dir, 'sample_0_after_relu1.pt'))
+# After maxpool
+x4 = model.maxpool(x3)
+save_tensor(x4[0], os.path.join(out_dir, 'sample_0_after_maxpool.pt'))
+# After layer1
+x5 = model.layer1(x4)
+save_tensor(x5[0], os.path.join(out_dir, 'sample_0_after_layer1.pt'))
+# After layer2
+x6 = model.layer2(x5)
+save_tensor(x6[0], os.path.join(out_dir, 'sample_0_after_layer2.pt'))
+# After layer3
+x7 = model.layer3(x6)
+save_tensor(x7[0], os.path.join(out_dir, 'sample_0_after_layer3.pt'))
+# After layer4
+x8 = model.layer4(x7)
+save_tensor(x8[0], os.path.join(out_dir, 'sample_0_after_layer4.pt'))
+
+print('Saved all intermediate outputs for sample 0 using C++-preprocessed input.') 
\ No newline at end of file
diff --git a/compare_resnet_weights.py b/compare_resnet_weights.py
new file mode 100644
index 0000000..d030a4d
--- /dev/null
+++ b/compare_resnet_weights.py
@@ -0,0 +1,36 @@
+import torch
+import os
+
+PYTHON_DIR = "exported_weights/backbone_pure_tensors/"
+CPP_DIR = "exported_weights/backbone_regenerated/"
+
+def compare_tensors(a, b, label):
+    a = a.float().cpu().contiguous().view(-1)
+    b = b.float().cpu().contiguous().view(-1)
+    if a.shape != b.shape:
+        print(f"{label}: Shape mismatch: {a.shape} vs {b.shape}")
+        return
+    cos_sim = torch.nn.functional.cosine_similarity(a, b, dim=0).item()
+    mae = torch.mean(torch.abs(a - b)).item()
+    max_abs = torch.max(torch.abs(a - b)).item()
+    print(f"{label}: cos_sim={cos_sim:.8f}, MAE={mae:.8e}, max_abs={max_abs:.8e}")
+
+def main():
+    py_files = {f for f in os.listdir(PYTHON_DIR) if f.endswith('.pt')}
+    cpp_files = {f for f in os.listdir(CPP_DIR) if f.endswith('.pt')}
+    common_files = sorted(py_files & cpp_files)
+    missing_in_cpp = sorted(py_files - cpp_files)
+    missing_in_py = sorted(cpp_files - py_files)
+
+    if missing_in_cpp:
+        print("Files missing in C++ export:", missing_in_cpp)
+    if missing_in_py:
+        print("Files missing in Python export:", missing_in_py)
+
+    for fname in common_files:
+        py_tensor = torch.load(os.path.join(PYTHON_DIR, fname), map_location='cpu', weights_only=False)
+        cpp_tensor = torch.load(os.path.join(CPP_DIR, fname), map_location='cpu', weights_only=False)
+        compare_tensors(py_tensor, cpp_tensor, fname)
+
+if __name__ == "__main__":
+    main() 
\ No newline at end of file
diff --git a/ltr/models/backbone/__pycache__/resnet.cpython-311.pyc b/ltr/models/backbone/__pycache__/resnet.cpython-311.pyc
index 62ac118..007b590 100644
Binary files a/ltr/models/backbone/__pycache__/resnet.cpython-311.pyc and b/ltr/models/backbone/__pycache__/resnet.cpython-311.pyc differ
diff --git a/ltr/models/backbone/__pycache__/resnet.cpython-37.pyc b/ltr/models/backbone/__pycache__/resnet.cpython-37.pyc
index c321d40..1fa149f 100644
Binary files a/ltr/models/backbone/__pycache__/resnet.cpython-37.pyc and b/ltr/models/backbone/__pycache__/resnet.cpython-37.pyc differ
diff --git a/ltr/models/backbone/resnet.py b/ltr/models/backbone/resnet.py
index fdc3a56..7fd3fcc 100644
--- a/ltr/models/backbone/resnet.py
+++ b/ltr/models/backbone/resnet.py
@@ -2,6 +2,8 @@ import math
 import torch.nn as nn
 from collections import OrderedDict
 import torch.utils.model_zoo as model_zoo
+import torch
+import os
 
 from .base import Backbone
 
@@ -119,32 +121,47 @@ class ResNet(Backbone):
         if output_layers is None:
             output_layers = self.output_layers
 
+        # Debug directory
+        debug_dir = 'test/output_py/resnet_py/sample_0/'
+        if not os.path.exists(debug_dir):
+            os.makedirs(debug_dir)
+        sample_idx = 0  # Always save for first sample
+
+        # conv1
         x = self.conv1(x)
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'debug_resnet_conv1_output_for_bn1_input.pt'))
         x = self.bn1(x)
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'bn1_output.pt'))
         x = self.relu(x)
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'relu1_output.pt'))
 
         if self._add_output_and_check('conv1', x, outputs, output_layers):
             return outputs
 
         x = self.maxpool(x)
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'maxpool_output.pt'))
 
+        # layer1
         x = self.layer1(x)
-
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'layer1_output.pt'))
         if self._add_output_and_check('layer1', x, outputs, output_layers):
             return outputs
 
+        # layer2
         x = self.layer2(x)
-
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'layer2_output.pt'))
         if self._add_output_and_check('layer2', x, outputs, output_layers):
             return outputs
 
+        # layer3
         x = self.layer3(x)
-
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'layer3_output.pt'))
         if self._add_output_and_check('layer3', x, outputs, output_layers):
             return outputs
 
+        # layer4
         x = self.layer4(x)
-
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'layer4_output.pt'))
         if self._add_output_and_check('layer4', x, outputs, output_layers):
             return outputs
 
diff --git a/ltr/models/bbreg/__pycache__/atom_iou_net.cpython-37.pyc b/ltr/models/bbreg/__pycache__/atom_iou_net.cpython-37.pyc
index 236e93d..80c7262 100644
Binary files a/ltr/models/bbreg/__pycache__/atom_iou_net.cpython-37.pyc and b/ltr/models/bbreg/__pycache__/atom_iou_net.cpython-37.pyc differ
diff --git a/test/compare_models.py b/test/compare_models.py
index 234343b..6b2a2e6 100644
--- a/test/compare_models.py
+++ b/test/compare_models.py
@@ -26,7 +26,7 @@ from pytracking.features.net_wrappers import DiMPTorchScriptWrapper
 # For loading AtomIoUNet from source
 from ltr.models.bbreg.atom_iou_net import AtomIoUNet
 # Add import for new modular comparison
-from model_comparison.bbreg_comparison import compare_debug_tensors
+from model_comparison.bbreg_comparison import compare_debug_tensors, compare_resnet_debug_tensors
 
 SCRIPT_DIR_FOR_INIT = os.path.dirname(os.path.abspath(__file__))
 ROOT_DIR_FOR_INIT = os.path.dirname(SCRIPT_DIR_FOR_INIT)
@@ -764,13 +764,12 @@ class ComparisonRunner:
                 processed_input_tensor = input_tensor.to(self.device) # Ensure device
             # --- END REINSTATED INPUT LOADING AND PREPROCESSING ---
 
-            # --- Save preprocessed input for sample 0 ---
-            if sample_idx == 0:
-                preprocessed_dir = Path(self.cpp_output_dir) / 'resnet'
-                preprocessed_dir.mkdir(parents=True, exist_ok=True)
-                py_preprocessed_path = preprocessed_dir / f'sample_{sample_idx}_image_preprocessed_python.pt'
-                torch.save(processed_input_tensor.cpu(), py_preprocessed_path)
-                print(f"Saved Python preprocessed image for sample {sample_idx} to {py_preprocessed_path}")
+            # --- Save preprocessed input for every sample ---
+            preprocessed_dir = Path(self.cpp_output_dir) / 'resnet'
+            preprocessed_dir.mkdir(parents=True, exist_ok=True)
+            py_preprocessed_path = preprocessed_dir / f'sample_{sample_idx}_image_preprocessed_python.pt'
+            torch.save(processed_input_tensor.cpu(), py_preprocessed_path)
+            print(f"Saved Python preprocessed image for sample {sample_idx} to {py_preprocessed_path}")
             # --- END save preprocessed input ---
 
             # Initialize dictionaries to store Python-side outputs for the current sample
@@ -987,6 +986,16 @@ class ComparisonRunner:
             # processed_samples += 1 # This variable is no longer used as loop is range-based
         print("--- ResNet Output Comparison Complete ---")
 
+    def compare_resnet_debug_outputs(self, sample_idx=0, verbose=True):
+        """
+        Compare intermediate ResNet debug outputs between C++ and Python and store results.
+        """
+        cpp_dir = os.path.join(self.cpp_output_dir, 'resnet')
+        py_dir = os.path.join(self.python_output_dir, 'resnet_debug')
+        results = compare_resnet_debug_tensors(cpp_dir, py_dir, sample_idx=sample_idx, verbose=verbose)
+        self.resnet_debug_results = results
+        self.all_comparison_stats['ResNetDebug'] = results
+
     def generate_html_report(self):
         print("\nGenerating HTML report...")
         report_path = os.path.join(self.comparison_dir, "report.html")
@@ -1260,6 +1269,23 @@ class ComparisonRunner:
             f.write(html_content)
         print(f"HTML report generated at {report_path}")
 
+        # Add ResNet Debug Output Comparison Section
+        if 'ResNetDebug' in self.all_comparison_stats:
+            html_content += "<h2>ResNet Intermediate Debug Output Comparison</h2>"
+            html_content += "<table><tr><th>Stage</th><th>Cosine Similarity</th><th>Allclose</th><th>Max Abs Diff</th><th>CPP Shape</th><th>PY Shape</th></tr>"
+            for stage, stats in self.all_comparison_stats['ResNetDebug'].items():
+                html_content += f"<tr><td>{stage}</td><td>{stats['cosine_similarity']:.6f}</td><td>{stats['allclose']}</td><td>{stats['max_abs_diff']:.6f}</td><td>{stats['cpp_shape']}</td><td>{stats['py_shape']}</td></tr>"
+            html_content += "</table>"
+
+        # ... rest of HTML ...
+        html_content += "</body></html>"
+        # Save HTML
+        report_path = os.path.join(self.comparison_dir, "comparison_report.html")
+        with open(report_path, "w") as f:
+            f.write(html_content)
+        print(f"HTML report generated at: {report_path}")
+        return report_path
+
     def _generate_single_plot(self, error_array, title, plot_path, mean_val, std_abs_err, mae, max_err):
         if error_array is None or len(error_array) == 0 or np.all(np.isnan(error_array)):
             # print(f"Skipping plot for {title} as error_array is empty or all NaNs.")
@@ -1287,6 +1313,10 @@ class ComparisonRunner:
         self.compare_classifier()
         self.compare_bb_regressor()
         self.compare_preprocessed_inputs() # ADDED
+        # Compare ResNet debug outputs before generating HTML
+        print("\nComparing ResNet intermediate debug outputs...")
+        self.compare_resnet_debug_outputs(sample_idx=0, verbose=True)
+        # ... rest of tests ...
         self.generate_html_report()
         print("All tests completed!")
 
diff --git a/test/exported_weights/backbone_pure_tensors/bn1_bias.pt b/test/exported_weights/backbone_pure_tensors/bn1_bias.pt
new file mode 100644
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diff --git a/test/model_comparison/bbreg_comparison.py b/test/model_comparison/bbreg_comparison.py
index f99fd33..1cdfd86 100644
--- a/test/model_comparison/bbreg_comparison.py
+++ b/test/model_comparison/bbreg_comparison.py
@@ -3,22 +3,31 @@ import torch
 import numpy as np
 
 def extract_tensor_from_jit(obj):
-    # Try to extract a tensor from a TorchScript module
+    """Extract a tensor from a TorchScript RecursiveScriptModule or return the tensor if already a tensor."""
+    import torch
     if isinstance(obj, torch.Tensor):
         return obj
-    # Try .tensor attribute (used in some containers)
-    if hasattr(obj, 'tensor') and isinstance(obj.tensor, torch.Tensor):
-        return obj.tensor
-    # Try named_parameters
-    if hasattr(obj, 'named_parameters'):
-        params = list(obj.named_parameters())
-        if params:
-            return params[0][1]
-    # Try named_buffers
-    if hasattr(obj, 'named_buffers'):
-        bufs = list(obj.named_buffers())
-        if bufs:
-            return bufs[0][1]
+    # Try common attribute names
+    for attr in ["tensor", "weight", "value"]:
+        if hasattr(obj, attr):
+            t = getattr(obj, attr)
+            if isinstance(t, torch.Tensor):
+                return t
+    # Try named_parameters and named_buffers
+    if hasattr(obj, "named_parameters"):
+        for name, param in obj.named_parameters():
+            if isinstance(param, torch.Tensor):
+                return param
+    if hasattr(obj, "named_buffers"):
+        for name, buf in obj.named_buffers():
+            if isinstance(buf, torch.Tensor):
+                return buf
+    # Try children recursively
+    if hasattr(obj, "children"):
+        for child in obj.children():
+            t = extract_tensor_from_jit(child)
+            if t is not None:
+                return t
     return None
 
 def compare_debug_tensors(cpp_dir, py_dir, sample_idx=0, verbose=True):
@@ -43,7 +52,7 @@ def compare_debug_tensors(cpp_dir, py_dir, sample_idx=0, verbose=True):
             py_tensor = None
             if os.path.exists(cpp_file):
                 try:
-                    obj = torch.load(cpp_file, map_location="cpu", weights_only=False)
+                    obj = torch.load(cpp_file, map_location="cpu")
                     cpp_tensor = extract_tensor_from_jit(obj)
                 except Exception as e:
                     print(f"[WARN] Could not load {cpp_file}: {e}")
@@ -51,7 +60,7 @@ def compare_debug_tensors(cpp_dir, py_dir, sample_idx=0, verbose=True):
                 print(f"[WARN] Missing file: {cpp_file}")
             if os.path.exists(py_file):
                 try:
-                    obj = torch.load(py_file, map_location="cpu", weights_only=False)
+                    obj = torch.load(py_file, map_location="cpu")
                     py_tensor = extract_tensor_from_jit(obj)
                 except Exception as e:
                     print(f"[WARN] Could not load {py_file}: {e}")
@@ -67,4 +76,84 @@ def compare_debug_tensors(cpp_dir, py_dir, sample_idx=0, verbose=True):
             results[(layer, stage)] = cos_sim
             if verbose:
                 print(f"Cosine similarity for {layer} {stage}: {cos_sim:.6f}")
+    return results
+
+def compare_resnet_debug_tensors(cpp_dir, py_dir, sample_idx=0, verbose=True):
+    """
+    Compare C++ and Python debug tensors for ResNet (after conv1, bn1, relu, maxpool, layer1, layer2, layer3, layer4).
+    Args:
+        cpp_dir (str): Directory with C++ debug tensors.
+        py_dir (str): Directory with Python debug tensors.
+        sample_idx (int): Sample index to compare.
+        verbose (bool): Print detailed comparison results.
+    Returns:
+        dict: Comparison metrics for each stage.
+    """
+    # Map stage to (cpp_filename, py_filename)
+    stage_map = {
+        "after_conv1":      (f"sample_{sample_idx}_conv1_output.pt",      f"sample_{sample_idx}_after_conv1.pt"),
+        "after_bn1":        (f"sample_{sample_idx}_bn1_output.pt",        f"sample_{sample_idx}_after_bn1.pt"),
+        "after_relu1":      (f"sample_{sample_idx}_relu1_output.pt",      f"sample_{sample_idx}_after_relu1.pt"),
+        "after_maxpool":    (f"sample_{sample_idx}_maxpool_output.pt",    f"sample_{sample_idx}_after_maxpool.pt"),
+        "after_layer1":     (f"sample_{sample_idx}_layer1.pt",            f"sample_{sample_idx}_after_layer1.pt"),
+        "after_layer2":     (f"sample_{sample_idx}_layer2.pt",            f"sample_{sample_idx}_after_layer2.pt"),
+        "after_layer3":     (f"sample_{sample_idx}_layer3.pt",            f"sample_{sample_idx}_after_layer3.pt"),
+        "after_layer4":     (f"sample_{sample_idx}_layer4.pt",            f"sample_{sample_idx}_after_layer4.pt"),
+    }
+    results = {}
+    import torch, os
+    for stage, (cpp_file, py_file) in stage_map.items():
+        cpp_path = os.path.join(cpp_dir, cpp_file)
+        py_path = os.path.join(py_dir, py_file)
+        cpp_tensor = None
+        py_tensor = None
+        if os.path.exists(cpp_path):
+            cpp_obj = torch.load(cpp_path, map_location='cpu', weights_only=False)
+            cpp_tensor = extract_tensor_from_jit(cpp_obj)
+        else:
+            if verbose:
+                print(f"[WARN] Missing file: {cpp_path}")
+        if os.path.exists(py_path):
+            py_obj = torch.load(py_path, map_location='cpu')
+            py_tensor = extract_tensor_from_jit(py_obj)
+        else:
+            if verbose:
+                print(f"[WARN] Missing file: {py_path}")
+        if cpp_tensor is None or py_tensor is None:
+            results[stage] = {
+                'cosine_similarity': None,
+                'allclose': None,
+                'max_abs_diff': None,
+                'cpp_shape': None if cpp_tensor is None else tuple(cpp_tensor.shape),
+                'py_shape': None if py_tensor is None else tuple(py_tensor.shape),
+            }
+            if verbose:
+                print(f"Warning: Cannot compare 'ResNet Debug {stage}' for sample {sample_idx}, one or both tensors are None.")
+            continue
+        # Flatten for cosine similarity
+        cpp_flat = cpp_tensor.flatten().float()
+        py_flat = py_tensor.flatten().float()
+        if cpp_flat.shape != py_flat.shape:
+            results[stage] = {
+                'cosine_similarity': None,
+                'allclose': False,
+                'max_abs_diff': None,
+                'cpp_shape': tuple(cpp_tensor.shape),
+                'py_shape': tuple(py_tensor.shape),
+            }
+            if verbose:
+                print(f"[WARN] Shape mismatch for {stage}: C++ {cpp_tensor.shape}, Py {py_tensor.shape}")
+            continue
+        cos_sim = torch.nn.functional.cosine_similarity(cpp_flat, py_flat, dim=0, eps=1e-8).item()
+        allclose = torch.allclose(cpp_tensor, py_tensor, atol=1e-5, rtol=1e-3)
+        max_abs_diff = (cpp_tensor - py_tensor).abs().max().item()
+        results[stage] = {
+            'cosine_similarity': cos_sim,
+            'allclose': allclose,
+            'max_abs_diff': max_abs_diff,
+            'cpp_shape': tuple(cpp_tensor.shape),
+            'py_shape': tuple(py_tensor.shape),
+        }
+        if verbose:
+            print(f"ResNet Debug {stage}: cos_sim={cos_sim:.6f}, allclose={allclose}, max_abs_diff={max_abs_diff:.3e}, shape={cpp_tensor.shape}")
     return results 
\ No newline at end of file
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diff --git a/test/test_models.cpp b/test/test_models.cpp
index b8ae426..bad9163 100644
--- a/test/test_models.cpp
+++ b/test/test_models.cpp
@@ -286,19 +286,22 @@ int main(int argc, char* argv[]) {
         torch::Tensor bb_tensor = get_or_generate_tensor(bb_file, {1, 4}, device, force_generate_dummy && !fs::exists(bb_file)); 
         torch::Tensor proposals_tensor = get_or_generate_tensor(proposals_file, {1, 10, 4}, device, force_generate_dummy && !fs::exists(proposals_file)); 
 
-        // --- Normalize the image tensor --- START
-        if (image_tensor.dim() == 3) { // Add batch dim if not present
-            image_tensor = image_tensor.unsqueeze(0);
+        // Try to load preprocessed input tensor from Python if it exists
+        fs::path py_preprocessed_path = resnet_out_dir / ("sample_" + std::to_string(i) + "_image_preprocessed_python.pt");
+        if (fs::exists(py_preprocessed_path)) {
+            std::cout << "Loading preprocessed input from Python: " << py_preprocessed_path.string() << std::endl;
+            image_tensor = load_tensor_from_file(py_preprocessed_path.string(), device);
+        } else {
+            // Fallback: generate or preprocess as before
+            if (image_tensor.dim() == 3) { // Add batch dim if not present
+                image_tensor = image_tensor.unsqueeze(0);
+            }
+            image_tensor = image_tensor / 255.0f;
+            torch::Tensor mean = torch::tensor({0.485, 0.456, 0.406}, image_tensor.options()).view({1, -1, 1, 1});
+            torch::Tensor std_dev = torch::tensor({0.229, 0.224, 0.225}, image_tensor.options()).view({1, -1, 1, 1});
+            image_tensor.sub_(mean).div_(std_dev);
         }
-        // Ensure it's float (already done by get_or_generate_tensor if is_image=true)
-        image_tensor = image_tensor / 255.0f; // Add division by 255.0
-
-        torch::Tensor mean = torch::tensor({0.485, 0.456, 0.406}, image_tensor.options()).view({1, -1, 1, 1});
-        torch::Tensor std_dev = torch::tensor({0.229, 0.224, 0.225}, image_tensor.options()).view({1, -1, 1, 1});
-        image_tensor.sub_(mean).div_(std_dev);
-        // --- Normalize the image tensor --- END
-
-        // Save C++ preprocessed input tensor
+        // Save C++ preprocessed input tensor (for record-keeping)
         fs::path cpp_preprocessed_save_path = resnet_out_dir / ("sample_" + std::to_string(i) + "_image_preprocessed_cpp.pt");
         save_tensor_to_file(image_tensor, cpp_preprocessed_save_path.string());
         std::cout << "Saved C++ preprocessed image for sample " << i << " to " << cpp_preprocessed_save_path.string() << std::endl;