diff --git a/test/test_models.cpp b/test/test_models.cpp
index 9415cfc..28c69d5 100644
--- a/test/test_models.cpp
+++ b/test/test_models.cpp
@@ -275,6 +275,8 @@ int main(int argc, char* argv[]) {
         if (bb_regressor_model_opt_wrapped.has_value() && resnet_outputs.count("layer2") && resnet_outputs.count("layer3")) { 
             try {
                 std::cout << "Processing BBRegressor for sample " << i << std::endl;
+                // Remove debug_get_conv3_1t_output and debug_get_conv4_1t_output calls (they do not exist)
+                // The correct BBRegressor logic is:
                 std::vector<torch::Tensor> backbone_feats_for_bb = {
                     resnet_outputs["layer2"].clone(), 
                     resnet_outputs["layer3"].clone()  
@@ -294,14 +296,6 @@ int main(int argc, char* argv[]) {
                 } else {
                     std::cerr << "  Skipping BBRegressor predict_iou for sample " << i << " (iou_feats or mod_vectors empty)." << std::endl;
                 }
-
-                // Save debug intermediate outputs
-                torch::Tensor cpp_conv3_1t_out = (*bb_regressor_model_opt_wrapped).debug_get_conv3_1t_output(resnet_outputs["layer2"].clone());
-                save_tensor_to_file(cpp_conv3_1t_out, (bb_reg_out_dir / (sample_suffix + "_debug_conv3_1t_output.pt")).string());
-
-                torch::Tensor cpp_conv4_1t_out = (*bb_regressor_model_opt_wrapped).debug_get_conv4_1t_output(resnet_outputs["layer3"].clone());
-                save_tensor_to_file(cpp_conv4_1t_out, (bb_reg_out_dir / (sample_suffix + "_debug_conv4_1t_output.pt")).string());
-
                 std::cout << "BBRegressor processing done for sample " << i << std::endl;
             } catch (const std::exception& e) {
                 std::cerr << "Error during BBRegressor processing for sample " << i << ": " << e.what() << std::endl;