diff --git a/jetson/power_logging/model/benchmark_pruned_model.py b/jetson/power_logging/model/benchmark_pruned_model.py
index 558f8bf4..b878e43a 100644
--- a/jetson/power_logging/model/benchmark_pruned_model.py
+++ b/jetson/power_logging/model/benchmark_pruned_model.py
@@ -180,7 +180,7 @@ def benchmark(args: argparse.Namespace) -> None:
         #     amount=0.5,
         # )
         # Thus should load the pruned yolo model
-        model = torch.load("pruned_30_yolov5su.pt")
+        model = torch.load("yolov5su.pt")
         model.eval().to(DEVICE)
 
         dtype = torch.float32
diff --git a/jetson/power_logging/run_experiment.sh b/jetson/power_logging/run_experiment.sh
index ec9d7f1f..61b06752 100755
--- a/jetson/power_logging/run_experiment.sh
+++ b/jetson/power_logging/run_experiment.sh
@@ -23,7 +23,7 @@ sleep 120
 # NOTE : fcn_resnet50 is a object detection model and does not work with TorchTensorRT library
 # TODO: Revisit fcn_resnet50 once this issue is addressed: https://github.com/pytorch/TensorRT/issues/3295
 #models=("alexnet" "vgg11" "vgg13" "vgg16" "vgg19" "mobilenet_v2" "mobilenet_v3_small" "mobilenet_v3_large" "resnet18" "resnet34" "resnet50" "resnet101" "resnet152" "lenet" "resnext50_32x4d" "resnext101_32x8d" "resnext101_64x4d" "convnext_tiny" "convnext_small" "convnext_base")
-models=("lenet")
+models=("yolov5s")
 # Number of inference cycles
 RUNS=30000
 
@@ -36,7 +36,7 @@ do
   if [ "$model" == "lenet" ]; then
     INPUT_SHAPE='--input-shape 1 1 32 32'
   else
-    INPUT_SHAPE='--input-shape 1 3 224 224'
+    INPUT_SHAPE='--input-shape 1 3 640 640'
   fi
 
   # Run the measure_inference_power.py script