diff --git a/Dockerfile.base b/Dockerfile.base
index c5f200bc..0fd950d2 100644
--- a/Dockerfile.base
+++ b/Dockerfile.base
@@ -45,6 +45,11 @@ RUN wget https://github.com/riscv-collab/riscv-gnu-toolchain/releases/download/2
 # Install torchsim dependency
 RUN apt install ninja-build && pip install onnx matplotlib && pip install --user conan==1.56.0 && pip install "transformers<4.44" && pip install diffusers==0.34.0
 
+# Extra Python deps for YOLO/vision tests
+RUN python -m pip install -U pip setuptools wheel && \
+    python -m pip install --no-cache-dir --no-deps ultralytics && \
+    python -m pip install --no-cache-dir opencv-python-headless pandas seaborn
+
 ENV RISCV=/workspace/riscv
 ENV PATH=$RISCV/bin:$PATH
 
diff --git a/tests/test_yolov5.py b/tests/test_yolov5.py
new file mode 100644
index 00000000..197b597a
--- /dev/null
+++ b/tests/test_yolov5.py
@@ -0,0 +1,88 @@
+import torch
+import torch._dynamo
+import torch.utils.cpp_extension
+
+import argparse
+import datetime
+
+import requests
+from PIL import Image
+from io import BytesIO
+from torchvision import transforms
+
+import os
+import shutil
+
+
+
+def run_yolo(batch, config):
+    from Scheduler.scheduler import PyTorchSimRunner
+    device = PyTorchSimRunner.setup_device().custom_device()
+
+    torch._dynamo.config.recompile_limit = 64
+    torch._dynamo.config.cache_size_limit = 128
+    
+    model = torch.hub.load("ultralytics/yolov5", "yolov5s").cpu().eval()
+    url = "https://ultralytics.com/images/zidane.jpg"
+    
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content)).convert("RGB")
+    
+    imgsz = 64    # 이미지 사이즈 줄여서 시뮬레이터 체크 가속
+    transform = transforms.Compose([
+        transforms.Resize((imgsz, imgsz)),
+        transforms.ToTensor(),
+    ])
+    
+    x = transform(img).unsqueeze(0)   # [1, 3, H, W]
+    x = x.to(device)
+    
+
+    model.to(device)
+    x = x.to(device)
+    
+    # Compile and run the model with PyTorchSim
+    compiled_model = torch.compile(dynamic=False)(model)
+    y = compiled_model(x)
+    print("Yolo Simulation Done")
+
+
+if __name__ == "__main__":
+    import sys
+
+    base_dir = os.environ.get("TORCHSIM_DIR", default="/workspace/PyTorchSim")
+    config = os.environ.get(
+        "TORCHSIM_CONFIG",
+        default=f"{base_dir}/configs/systolic_ws_128x128_c1_simple_noc_tpuv3.yml",
+    )
+    config_prefix = config.split("/")[-1].split(".")[0][
+        9:
+    ]  # extract config name from config path
+    sys.path.append(base_dir)
+    args = argparse.ArgumentParser()
+    args.add_argument("--batch", type=int, default=1)
+    args.add_argument("--dump_path", type=str, default="results")
+    args = args.parse_args()
+    batch = args.batch
+    result_path = os.path.join(
+        base_dir,
+        args.dump_path,
+        config_prefix,
+        f"yolo5s_{batch}_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}",
+    )
+    
+    
+    # setting environment variables
+    os.environ["TORCHSIM_LOG_PATH"] = result_path
+    os.environ["TORCHSIM_USE_TIMING_POOLING"] = "1"
+    
+    # only timing simulation
+    os.environ["TORCHSIM_VALIDATION_MODE"] = "0"
+    if "pytorchsim_functional_mode" in os.environ:
+        del os.environ["pytorchsim_functional_mode"]
+
+    # Clear extension/inductor caches to force rebuilds
+    shutil.rmtree("/tmp/torchinductor_root", ignore_errors=True)
+    shutil.rmtree(os.path.expanduser("~/.cache/torch_extensions/py311_cu126/npu"), ignore_errors=True)
+
+    run_yolo(batch, config)