  The label-free model evaluation aims to predict the model performance on
various test sets without relying on ground truths. The main challenge of this
task is the absence of labels in the test data, unlike in classical supervised
model evaluation. This paper presents our solutions for the 1st DataCV
Challenge of the Visual Dataset Understanding workshop at CVPR 2023. Firstly,
we propose a novel method called K-means Clustering Based Feature Consistency
Alignment (KCFCA), which is tailored to handle the distribution shifts of
various datasets. KCFCA utilizes the K-means algorithm to cluster labeled
training sets and unlabeled test sets, and then aligns the cluster centers with
feature consistency. Secondly, we develop a dynamic regression model to capture
the relationship between the shifts in distribution and model accuracy.
Thirdly, we design an algorithm to discover the outlier model factors,
eliminate the outlier models, and combine the strengths of multiple autoeval
models. On the DataCV Challenge leaderboard, our approach secured 2nd place
with an RMSE of 6.8526. Our method significantly improved over the best
baseline method by 36\% (6.8526 vs. 10.7378). Furthermore, our method achieves
a relatively more robust and optimal single model performance on the validation
dataset.
