diff --git a/examples/cifar/README.md b/examples/cifar/README.md
index a6ae155..652a5b3 100644
--- a/examples/cifar/README.md
+++ b/examples/cifar/README.md
@@ -38,7 +38,23 @@ python analyze.py --query_batch_size 1000 \
 In addition to `ekfac`, you can also use `identity`, `diagonal`, and `kfac` as the `factor_strategy`. On an A100 (80GB) GPU, it takes roughly 2 minutes to compute the pairwise scores (including computing the EKFAC factors):
 
 ```
-
+----------------------------------------------------------------------------------------------------------------------------------
+|  Action                       |  Mean duration (s)    |  Num calls            |  Total time (s)       |  Percentage %         |
+----------------------------------------------------------------------------------------------------------------------------------
+|  Total                        |  -                    |  11                   |  112.83               |  100 %                |
+----------------------------------------------------------------------------------------------------------------------------------
+|  Compute Pairwise Score       |  47.989               |  1                    |  47.989               |  42.532               |
+|  Fit Lambda                   |  34.639               |  1                    |  34.639               |  30.7                 |
+|  Fit Covariance               |  21.841               |  1                    |  21.841               |  19.357               |
+|  Save Pairwise Score          |  3.5998               |  1                    |  3.5998               |  3.1905               |
+|  Perform Eigendecomposition   |  2.7724               |  1                    |  2.7724               |  2.4572               |
+|  Save Covariance              |  0.85695              |  1                    |  0.85695              |  0.75951              |
+|  Save Eigendecomposition      |  0.85628              |  1                    |  0.85628              |  0.75892              |
+|  Save Lambda                  |  0.12327              |  1                    |  0.12327              |  0.10925              |
+|  Load Eigendecomposition      |  0.056494             |  1                    |  0.056494             |  0.05007              |
+|  Load All Factors             |  0.048981             |  1                    |  0.048981             |  0.043412             |
+|  Load Covariance              |  0.046798             |  1                    |  0.046798             |  0.041476             |
+----------------------------------------------------------------------------------------------------------------------------------
 ```
 
 To use AMP when computing influence scores (in addition to half precision when computing influence factors and scores), run:
diff --git a/examples/cifar/analyze.py b/examples/cifar/analyze.py
index 5731191..d628b96 100644
--- a/examples/cifar/analyze.py
+++ b/examples/cifar/analyze.py
@@ -151,10 +151,10 @@ def main():
         factor_args = all_low_precision_factor_arguments(strategy=args.factor_strategy, dtype=torch.bfloat16)
         factors_name += "_half"
     analyzer.fit_all_factors(
-        factors_name=args.factor_strategy,
+        factors_name=factors_name,
+        factor_args=factor_args,
         dataset=train_dataset,
         per_device_batch_size=None,
-        factor_args=factor_args,
         overwrite_output_dir=False,
     )