diff --git a/cluster_artist.py b/cluster_artist.py
deleted file mode 100644
index 9717034..0000000
--- a/cluster_artist.py
+++ /dev/null
@@ -1,251 +0,0 @@
-"""
-使用提取的特征进行画师风格聚类
-支持多种聚类算法和可视化
-"""
-import argparse
-import numpy as np
-import json
-from pathlib import Path
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
-from sklearn.manifold import TSNE
-from sklearn.decomposition import PCA
-import seaborn as sns
-
-
-def get_args_parser():
-    parser = argparse.ArgumentParser('Artist Style Clustering', add_help=False)
-    
-    parser.add_argument('--features', required=True, type=str,
-                        help='Path to features.npy file')
-    parser.add_argument('--image-names', required=True, type=str,
-                        help='Path to image_names.txt file')
-    parser.add_argument('--output', default='./output/clustering', type=str,
-                        help='Output directory')
-    
-    # 聚类参数
-    parser.add_argument('--method', default='kmeans', type=str,
-                        choices=['kmeans', 'dbscan', 'hierarchical'],
-                        help='Clustering method')
-    parser.add_argument('--n-clusters', default=10, type=int,
-                        help='Number of clusters (for kmeans and hierarchical)')
-    parser.add_argument('--eps', default=0.5, type=float,
-                        help='DBSCAN eps parameter')
-    parser.add_argument('--min-samples', default=5, type=int,
-                        help='DBSCAN min_samples parameter')
-    
-    # 可视化参数
-    parser.add_argument('--visualize', action='store_true', default=True,
-                        help='Create visualization')
-    parser.add_argument('--viz-method', default='tsne', type=str,
-                        choices=['tsne', 'pca'],
-                        help='Dimensionality reduction method for visualization')
-    parser.add_argument('--perplexity', default=30, type=int,
-                        help='t-SNE perplexity parameter')
-    
-    return parser
-
-
-def load_features(features_path, image_names_path):
-    """加载特征和图像名称"""
-    features = np.load(features_path)
-    with open(image_names_path, 'r') as f:
-        image_names = [line.strip() for line in f]
-    
-    print(f"Loaded features: {features.shape}")
-    print(f"Number of images: {len(image_names)}")
-    
-    return features, image_names
-
-
-def perform_clustering(features, method='kmeans', n_clusters=10, eps=0.5, min_samples=5):
-    """执行聚类"""
-    print(f"\nPerforming clustering with method: {method}")
-    
-    if method == 'kmeans':
-        clusterer = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
-        labels = clusterer.fit_predict(features)
-        print(f"K-Means clustering completed with {n_clusters} clusters")
-        
-    elif method == 'dbscan':
-        clusterer = DBSCAN(eps=eps, min_samples=min_samples)
-        labels = clusterer.fit_predict(features)
-        n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
-        n_noise = list(labels).count(-1)
-        print(f"DBSCAN clustering completed")
-        print(f"Number of clusters: {n_clusters}")
-        print(f"Number of noise points: {n_noise}")
-        
-    elif method == 'hierarchical':
-        clusterer = AgglomerativeClustering(n_clusters=n_clusters)
-        labels = clusterer.fit_predict(features)
-        print(f"Hierarchical clustering completed with {n_clusters} clusters")
-    
-    return labels
-
-
-def reduce_dimensions(features, method='tsne', perplexity=30):
-    """降维用于可视化"""
-    print(f"\nReducing dimensions with {method}")
-    
-    if method == 'tsne':
-        reducer = TSNE(n_components=2, perplexity=perplexity, random_state=42)
-        features_2d = reducer.fit_transform(features)
-        print("t-SNE reduction completed")
-        
-    elif method == 'pca':
-        reducer = PCA(n_components=2, random_state=42)
-        features_2d = reducer.fit_transform(features)
-        explained_var = reducer.explained_variance_ratio_
-        print(f"PCA reduction completed")
-        print(f"Explained variance: {explained_var[0]:.3f}, {explained_var[1]:.3f}")
-    
-    return features_2d
-
-
-def visualize_clusters(features_2d, labels, output_dir, method_name):
-    """可视化聚类结果"""
-    print("\nCreating visualization...")
-    
-    plt.figure(figsize=(12, 10))
-    
-    # 获取唯一的标签（聚类）
-    unique_labels = set(labels)
-    n_clusters = len(unique_labels) - (1 if -1 in unique_labels else 0)
-    
-    # 使用不同颜色
-    colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
-    
-    for label, color in zip(unique_labels, colors):
-        if label == -1:
-            # 噪声点用黑色
-            color = [0, 0, 0, 1]
-            marker = 'x'
-            label_name = 'Noise'
-        else:
-            marker = 'o'
-            label_name = f'Cluster {label}'
-        
-        mask = labels == label
-        plt.scatter(features_2d[mask, 0], features_2d[mask, 1],
-                   c=[color], label=label_name, marker=marker, s=50, alpha=0.6)
-    
-    plt.title(f'Artist Style Clustering ({method_name})\nTotal Clusters: {n_clusters}', 
-              fontsize=14, fontweight='bold')
-    plt.xlabel('Dimension 1', fontsize=12)
-    plt.ylabel('Dimension 2', fontsize=12)
-    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=10)
-    plt.tight_layout()
-    
-    # 保存图像
-    output_path = output_dir / f'clustering_{method_name}.png'
-    plt.savefig(output_path, dpi=300, bbox_inches='tight')
-    print(f"Visualization saved to: {output_path}")
-    
-    plt.close()
-
-
-def save_clustering_results(labels, image_names, output_dir):
-    """保存聚类结果"""
-    # 按聚类分组
-    clusters = {}
-    for label, name in zip(labels, image_names):
-        label = int(label)
-        if label not in clusters:
-            clusters[label] = []
-        clusters[label].append(name)
-    
-    # 保存JSON格式
-    json_path = output_dir / 'clustering_results.json'
-    with open(json_path, 'w', encoding='utf-8') as f:
-        json.dump(clusters, f, indent=2, ensure_ascii=False)
-    print(f"Clustering results saved to: {json_path}")
-    
-    # 保存文本格式（易读）
-    txt_path = output_dir / 'clustering_results.txt'
-    with open(txt_path, 'w', encoding='utf-8') as f:
-        for label in sorted(clusters.keys()):
-            if label == -1:
-                f.write(f"Noise ({len(clusters[label])} images):\n")
-            else:
-                f.write(f"Cluster {label} ({len(clusters[label])} images):\n")
-            for name in clusters[label]:
-                f.write(f"  - {name}\n")
-            f.write("\n")
-    print(f"Clustering results (text) saved to: {txt_path}")
-    
-    # 统计信息
-    stats = {
-        'total_images': len(image_names),
-        'n_clusters': len([k for k in clusters.keys() if k != -1]),
-        'cluster_sizes': {int(k): len(v) for k, v in clusters.items()}
-    }
-    
-    stats_path = output_dir / 'clustering_stats.json'
-    with open(stats_path, 'w', encoding='utf-8') as f:
-        json.dump(stats, f, indent=2)
-    print(f"Clustering statistics saved to: {stats_path}")
-    
-    return clusters
-
-
-def print_cluster_statistics(clusters):
-    """打印聚类统计信息"""
-    print("\n" + "="*50)
-    print("Clustering Statistics")
-    print("="*50)
-    
-    total_images = sum(len(v) for v in clusters.values())
-    n_clusters = len([k for k in clusters.keys() if k != -1])
-    
-    print(f"Total images: {total_images}")
-    print(f"Number of clusters: {n_clusters}")
-    
-    if -1 in clusters:
-        print(f"Noise points: {len(clusters[-1])}")
-    
-    print("\nCluster sizes:")
-    for label in sorted(clusters.keys()):
-        if label == -1:
-            print(f"  Noise: {len(clusters[label])} images")
-        else:
-            print(f"  Cluster {label}: {len(clusters[label])} images")
-    
-    print("="*50)
-
-
-def main(args):
-    # 创建输出目录
-    output_dir = Path(args.output)
-    output_dir.mkdir(parents=True, exist_ok=True)
-    
-    # 加载特征
-    features, image_names = load_features(args.features, args.image_names)
-    
-    # 执行聚类
-    labels = perform_clustering(
-        features, 
-        method=args.method,
-        n_clusters=args.n_clusters,
-        eps=args.eps,
-        min_samples=args.min_samples
-    )
-    
-    # 保存聚类结果
-    clusters = save_clustering_results(labels, image_names, output_dir)
-    
-    # 打印统计信息
-    print_cluster_statistics(clusters)
-    
-    # 可视化
-    if args.visualize:
-        features_2d = reduce_dimensions(features, method=args.viz_method, perplexity=args.perplexity)
-        visualize_clusters(features_2d, labels, output_dir, args.method)
-    
-    print(f"\n✓ Clustering completed! Results saved to: {output_dir}")
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser('Artist Style Clustering', parents=[get_args_parser()])
-    args = parser.parse_args()
-    main(args)
diff --git a/model/lsnet_artist.py b/model/lsnet_artist.py
index f03860f..d122a4c 100644
--- a/model/lsnet_artist.py
+++ b/model/lsnet_artist.py
@@ -129,8 +129,8 @@ default_cfgs_artist = dict(
     lsnet_t_artist = _cfg_artist(),
     lsnet_s_artist = _cfg_artist(),
     lsnet_b_artist = _cfg_artist(),
-    lsnet_l_artist = _cfg_artist(),  # Large model for massive training
-    lsnet_xl_artist = _cfg_artist(), # Extra Large model for 100k+ classes
+    lsnet_l_artist = _cfg_artist(),
+    lsnet_xl_artist = _cfg_artist(),
 )
 
 
@@ -151,7 +151,6 @@ def _create_lsnet_artist(variant, pretrained=False, **kwargs):
 @register_model
 def lsnet_t_artist(num_classes=1000, distillation=False, pretrained=False, 
                    feature_dim=None, use_projection=True, **kwargs):
-    """LSNet-T for Artist Style Classification"""
     model = _create_lsnet_artist(
         "lsnet_t_artist",
         pretrained=pretrained,
@@ -172,7 +171,6 @@ def lsnet_t_artist(num_classes=1000, distillation=False, pretrained=False,
 @register_model
 def lsnet_s_artist(num_classes=1000, distillation=False, pretrained=False,
                    feature_dim=None, use_projection=True, **kwargs):
-    """LSNet-S for Artist Style Classification"""
     model = _create_lsnet_artist(
         "lsnet_s_artist",
         pretrained=pretrained,
@@ -193,7 +191,6 @@ def lsnet_s_artist(num_classes=1000, distillation=False, pretrained=False,
 @register_model
 def lsnet_b_artist(num_classes=1000, distillation=False, pretrained=False,
                    feature_dim=None, use_projection=True, **kwargs):
-    """LSNet-B for Artist Style Classification"""
     model = _create_lsnet_artist(
         "lsnet_b_artist",
         pretrained=pretrained,
@@ -214,7 +211,6 @@ def lsnet_b_artist(num_classes=1000, distillation=False, pretrained=False,
 @register_model
 def lsnet_l_artist(num_classes=1000, distillation=False, pretrained=False,
                    feature_dim=None, use_projection=True, **kwargs):
-    """LSNet-L for Artist Style Classification (Large model for massive training)"""
     model = _create_lsnet_artist(
         "lsnet_l_artist",
         pretrained=pretrained,
@@ -235,7 +231,6 @@ def lsnet_l_artist(num_classes=1000, distillation=False, pretrained=False,
 @register_model
 def lsnet_xl_artist(num_classes=1000, distillation=False, pretrained=False,
                     feature_dim=None, use_projection=True, **kwargs):
-    """LSNet-XL for Artist Style Classification (Extra Large model for massive datasets with 100k+ classes)"""
     model = _create_lsnet_artist(
         "lsnet_xl_artist",
         pretrained=pretrained,