diff --git a/package/samplers/implicit_natural_gradient/README.md b/package/samplers/implicit_natural_gradient/README.md
index d8cb8b04..bca903bf 100644
--- a/package/samplers/implicit_natural_gradient/README.md
+++ b/package/samplers/implicit_natural_gradient/README.md
@@ -14,14 +14,36 @@ license: MIT License
 ## Example
 
 ```python
-mod = optunahub.load_module("samplers/implicit_natural_gradient")
-sampler = mod.ImplicitNaturalGradientSampler()
-```
+import optuna
+import optunahub
+
+
+def objective(trial: optuna.Trial) -> float:
+    x = trial.suggest_float("x", -100, 100)
+    y = trial.suggest_float("y", -100, 100)
+    return x**2 + y**2
+
+
+def main() -> None:
+    mod = optunahub.load_module("samplers/implicit_natural_gradient")
+
+    sampler = mod.ImplicitNaturalGradientSampler()
+    study = optuna.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=200)
 
-See [`example.py`](https://github.com/optuna/optunahub-registry/blob/main/package/samplers/implicit_natural_gradient/example.py) for more details.
+    print(study.best_trial.value, study.best_trial.params)
+
+
+if __name__ == "__main__":
+    main()
+```
 
 ## Others
 
+📝 [**A Natural Gradient-Based Optimization Algorithm Registered on OptunaHub**](https://medium.com/optuna/a-natural-gradient-based-optimization-algorithm-registered-on-optunahub-0dbe17cb0f7d): Blog post by Hiroki Takizawa. In the post, benchmark results are presented as shown in the figure below.
+
+![The performance comparison results of this sampler and CMA-ES](images/ingo-performance.png)
+
 ### Reference
 
 Yueming Lyu, Ivor W. Tsang (2019). Black-box Optimizer with Implicit Natural Gradient. arXiv:1910.04301
diff --git a/package/samplers/implicit_natural_gradient/images/ingo-performance.png b/package/samplers/implicit_natural_gradient/images/ingo-performance.png
new file mode 100644
index 00000000..d57706eb
Binary files /dev/null and b/package/samplers/implicit_natural_gradient/images/ingo-performance.png differ