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.gitattributes

@@ -37,8 +37,3 @@ checkpoint-80000/trainer_state.json filter=lfs diff=lfs merge=lfs -text
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-演示.pptx filter=lfs diff=lfs merge=lfs -text
-二阶段同步会议0521.pptx filter=lfs diff=lfs merge=lfs -text

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20250427_test.tar

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20250428/win_rate_category_score_comparisonv2.csv

@@ -1,26 +0,0 @@
-category,type,dir1_avg,dir2_avg,comparison,subcategory
-标牌标语,category,0.784,0.835,dir1 < dir2,
-手机屏拍,category,0.871,0.913,dir1 < dir2,
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-电脑屏拍,subcategory,0.867,0.916,<,网页
-标牌标语,subcategory,0.764,0.828,<,展板标语
-电脑屏拍,subcategory,0.833,0.897,<,应用
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-纸质文档,subcategory,0.819,0.857,<,试卷
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-手机屏拍,subcategory,0.865,0.922,<,聊天
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-手机屏拍,subcategory,0.878,0.931,<,提醒
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-电脑屏拍,subcategory,0.794,0.833,<,游戏
-标牌标语,subcategory,0.805,0.842,<,路标路牌

20250428/win_rate_comparisonv2.csv

@@ -1,26 +0,0 @@
-category,subcategory,win_rate,wins,total_comparisons
-商品包装,其他商品,0.1,1,10
-手机屏拍,网页,0.1,1,10
-手机屏拍,应用,0.2,2,10
-手机屏拍,系统,0.1,1,10
-商品包装,药品保健,0.4,4,10
-电脑屏拍,网页,0.1,1,10
-标牌标语,展板标语,0.4,4,10
-电脑屏拍,应用,0.0,0,10
-电脑屏拍,文件,0.2,2,10
-电脑屏拍,系统,0.0,0,10
-商品包装,食品饮品,0.0,0,9
-商品包装,美妆护肤,0.1,1,10
-纸质文档,试卷,0.1,1,10
-纸质文档,说明书,0.2,2,10
-手机屏拍,聊天,0.1,1,10
-纸质文档,菜单,0.1,1,10
-手机屏拍,提醒,0.0,0,10
-纸质文档,教材,0.3,3,10
-电脑屏拍,游戏,0.2,2,10
-标牌标语,路标路牌,0.4,4,10
-商品包装,ALL,0.15384615384615385,6,39
-手机屏拍,ALL,0.1,5,50
-电脑屏拍,ALL,0.1,5,50
-标牌标语,ALL,0.4,8,20
-纸质文档,ALL,0.175,7,40

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cluster_out_text_and_pic.tar

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data_zh_all.tar

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organized.tar

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organized_clusterv2.tar

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organized_domain_labels.tar

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screenshot_file.tar

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vs.tar

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win_rate_category_score_comparison.csv

@@ -1,26 +0,0 @@
-category,type,dir1_avg,dir2_avg,comparison,subcategory
-电脑屏拍,category,0.878,0.882,dir1 < dir2,
-纸质文档,category,0.846,0.859,dir1 < dir2,
-商品包装,category,0.839,0.854,dir1 < dir2,
-标牌标语,category,0.832,0.835,dir1 < dir2,
-手机屏拍,category,0.906,0.913,dir1 < dir2,
-纸质文档,subcategory,0.884,0.88,>,说明书
-纸质文档,subcategory,0.826,0.857,<,试卷
-商品包装,subcategory,0.822,0.829,<,药品保健
-电脑屏拍,subcategory,0.838,0.833,>,游戏
-手机屏拍,subcategory,0.905,0.922,<,聊天
-电脑屏拍,subcategory,0.894,0.916,<,网页
-电脑屏拍,subcategory,0.877,0.897,<,应用
-标牌标语,subcategory,0.825,0.828,<,展板标语
-电脑屏拍,subcategory,0.879,0.852,>,文件
-电脑屏拍,subcategory,0.903,0.913,<,系统
-商品包装,subcategory,0.82,0.844,<,其他商品
-纸质文档,subcategory,0.793,0.812,<,菜单
-手机屏拍,subcategory,0.88,0.894,<,网页
-手机屏拍,subcategory,0.898,0.892,>,应用
-标牌标语,subcategory,0.84,0.842,<,路标路牌
-商品包装,subcategory,0.864,0.868,<,美妆护肤
-手机屏拍,subcategory,0.929,0.931,<,提醒
-手机屏拍,subcategory,0.917,0.925,<,系统
-纸质文档,subcategory,0.881,0.886,<,教材
-商品包装,subcategory,0.851,0.874,<,食品饮品

win_rate_category_score_comparisonv2.csv

@@ -1,26 +0,0 @@
-category,type,dir1_avg,dir2_avg,comparison,subcategory
-标牌标语,category,0.784,0.835,dir1 < dir2,
-手机屏拍,category,0.871,0.913,dir1 < dir2,
-电脑屏拍,category,0.844,0.882,dir1 < dir2,
-商品包装,category,0.802,0.854,dir1 < dir2,
-纸质文档,category,0.818,0.859,dir1 < dir2,
-商品包装,subcategory,0.777,0.844,<,其他商品
-手机屏拍,subcategory,0.859,0.894,<,网页
-手机屏拍,subcategory,0.883,0.892,<,应用
-手机屏拍,subcategory,0.873,0.925,<,系统
-商品包装,subcategory,0.826,0.829,<,药品保健
-电脑屏拍,subcategory,0.867,0.916,<,网页
-标牌标语,subcategory,0.764,0.828,<,展板标语
-电脑屏拍,subcategory,0.833,0.897,<,应用
-电脑屏拍,subcategory,0.862,0.852,>,文件
-电脑屏拍,subcategory,0.867,0.913,<,系统
-商品包装,subcategory,0.808,0.874,<,食品饮品
-商品包装,subcategory,0.795,0.868,<,美妆护肤
-纸质文档,subcategory,0.819,0.857,<,试卷
-纸质文档,subcategory,0.842,0.88,<,说明书
-手机屏拍,subcategory,0.865,0.922,<,聊天
-纸质文档,subcategory,0.76,0.812,<,菜单
-手机屏拍,subcategory,0.878,0.931,<,提醒
-纸质文档,subcategory,0.85,0.886,<,教材
-电脑屏拍,subcategory,0.794,0.833,<,游戏
-标牌标语,subcategory,0.805,0.842,<,路标路牌

win_rate_comparison.csv

@@ -1,26 +0,0 @@
-category,subcategory,win_rate,wins,total_comparisons
-纸质文档,说明书,0.6,6,10
-纸质文档,试卷,0.2,2,10
-商品包装,药品保健,0.4,4,10
-电脑屏拍,游戏,0.3,3,10
-手机屏拍,聊天,0.1,1,10
-电脑屏拍,网页,0.3,3,10
-电脑屏拍,应用,0.4,4,10
-标牌标语,展板标语,0.6,6,10
-电脑屏拍,文件,0.7,7,10
-电脑屏拍,系统,0.3,3,10
-商品包装,其他商品,0.4,4,10
-纸质文档,菜单,0.4,4,10
-手机屏拍,网页,0.2,2,10
-手机屏拍,应用,0.3,3,10
-标牌标语,路标路牌,0.5,5,10
-商品包装,美妆护肤,0.3,3,10
-手机屏拍,提醒,0.4,4,10
-手机屏拍,系统,0.1,1,10
-纸质文档,教材,0.4,4,10
-商品包装,食品饮品,0.3333333333333333,3,9
-纸质文档,ALL,0.4,16,40
-商品包装,ALL,0.358974358974359,14,39
-电脑屏拍,ALL,0.4,20,50
-手机屏拍,ALL,0.22,11,50
-标牌标语,ALL,0.55,11,20

win_rate_comparisonv2.csv

@@ -1,26 +0,0 @@
-category,subcategory,win_rate,wins,total_comparisons
-商品包装,其他商品,0.1,1,10
-手机屏拍,网页,0.1,1,10
-手机屏拍,应用,0.2,2,10
-手机屏拍,系统,0.1,1,10
-商品包装,药品保健,0.4,4,10
-电脑屏拍,网页,0.1,1,10
-标牌标语,展板标语,0.4,4,10
-电脑屏拍,应用,0.0,0,10
-电脑屏拍,文件,0.2,2,10
-电脑屏拍,系统,0.0,0,10
-商品包装,食品饮品,0.0,0,9
-商品包装,美妆护肤,0.1,1,10
-纸质文档,试卷,0.1,1,10
-纸质文档,说明书,0.2,2,10
-手机屏拍,聊天,0.1,1,10
-纸质文档,菜单,0.1,1,10
-手机屏拍,提醒,0.0,0,10
-纸质文档,教材,0.3,3,10
-电脑屏拍,游戏,0.2,2,10
-标牌标语,路标路牌,0.4,4,10
-商品包装,ALL,0.15384615384615385,6,39
-手机屏拍,ALL,0.1,5,50
-电脑屏拍,ALL,0.1,5,50
-标牌标语,ALL,0.4,8,20
-纸质文档,ALL,0.175,7,40

winrate.py

@@ -1,271 +0,0 @@
-import os
-import pandas as pd
-from collections import defaultdict
-
-def calculate_avg_comet(root_dir):
-    results = []
-    
-    for category in os.listdir(root_dir):
-        category_path = os.path.join(root_dir, category)
-        if not os.path.isdir(category_path):
-            continue
-            
-        for subcategory in os.listdir(category_path):
-            subcategory_path = os.path.join(category_path, subcategory)
-            if not os.path.isdir(subcategory_path):
-                continue
-                
-            report_path = os.path.join(subcategory_path, 'report', 'full_image_comet.csv')
-            if os.path.exists(report_path):
-                try:
-                    df = pd.read_csv(report_path)
-                    avg_score = df['avg_comet_sentence'].mean()
-                    results.append({
-                        'category': category,
-                        'subcategory': subcategory,
-                        'avg_comet': avg_score
-                    })
-                except Exception as e:
-                    print(f"Error processing {report_path}: {e}")
-    
-    result_df = pd.DataFrame(results)
-    result_df.to_csv('comet_avg_scores.csv', index=False)
-    return result_df
-
-def compare_win_rate(dir1, dir2):
-    data1 = defaultdict(dict)
-    data2 = defaultdict(dict)
-    
-    # 读取第一个目录的数据
-    for category in os.listdir(dir1):
-        category_path = os.path.join(dir1, category)
-        if not os.path.isdir(category_path):
-            continue
-            
-        for subcategory in os.listdir(category_path):
-            subcategory_path = os.path.join(category_path, subcategory)
-            if not os.path.isdir(subcategory_path):
-                continue
-                
-            report_path = os.path.join(subcategory_path, 'report', 'full_image_comet.csv')
-            if os.path.exists(report_path):
-                try:
-                    df = pd.read_csv(report_path)
-                    for _, row in df.iterrows():
-                        data1[(category, subcategory)][row['file_name']] = row['avg_comet_sentence']
-                except Exception as e:
-                    print(f"Error processing {report_path}: {e}")
-    
-    # 读取第二个目录的数据
-    for category in os.listdir(dir2):
-        category_path = os.path.join(dir2, category)
-        if not os.path.isdir(category_path):
-            continue
-            
-        for subcategory in os.listdir(category_path):
-            subcategory_path = os.path.join(category_path, subcategory)
-            if not os.path.isdir(subcategory_path):
-                continue
-                
-            report_path = os.path.join(subcategory_path, 'report', 'full_image_comet.csv')
-            if os.path.exists(report_path):
-                try:
-                    df = pd.read_csv(report_path)
-                    for _, row in df.iterrows():
-                        data2[(category, subcategory)][row['file_name']] = row['avg_comet_sentence']
-                except Exception as e:
-                    print(f"Error processing {report_path}: {e}")
-    
-    # 计算win rate
-    win_rate_results = []
-    category_stats = defaultdict(lambda: {'wins': 0, 'total': 0})
-    
-    for key in set(data1.keys()).union(set(data2.keys())):
-        category, subcategory = key
-        scores1 = data1.get(key, {})
-        scores2 = data2.get(key, {})
-        
-        common_files = set(scores1.keys()).intersection(set(scores2.keys()))
-        if not common_files:
-            continue
-            
-        wins = 0
-        total = 0
-        
-        for file in common_files:
-            total += 1
-            if scores1[file] > scores2[file]:
-                wins += 1
-        
-        win_rate = wins / total if total > 0 else 0
-        win_rate_results.append({
-            'category': category,
-            'subcategory': subcategory,
-            'win_rate': win_rate,
-            'wins': wins,
-            'total_comparisons': total
-        })
-        
-        # 更新大类统计
-        category_stats[category]['wins'] += wins
-        category_stats[category]['total'] += total
-    
-    # 添加大类统计结果
-    for category, stats in category_stats.items():
-        cat_win_rate = stats['wins'] / stats['total'] if stats['total'] > 0 else 0
-        win_rate_results.append({
-            'category': category,
-            'subcategory': 'ALL',
-            'win_rate': cat_win_rate,
-            'wins': stats['wins'],
-            'total_comparisons': stats['total']
-        })
-    
-    # 创建DataFrame并保存
-    win_rate_df = pd.DataFrame(win_rate_results)
-    win_rate_df.to_csv('win_rate_comparison.csv', index=False)
-    
-    # 打印统计数据
-    print("\n=== 大类统计结果 ===")
-    for category, stats in category_stats.items():
-        win_rate = stats['wins'] / stats['total'] if stats['total'] > 0 else 0
-        print(f"大类: {category}")
-        print(f"总比较次数: {stats['total']}")
-        print(f"获胜次数: {stats['wins']}")
-        print(f"Win Rate: {win_rate:.2%}")
-        print("-" * 30)
-    
-    return win_rate_df
-def compare_category_scores(dir1, dir2):
-    """
-    比较两个目录中每个大类和子类的平均分数，并输出总结性比较
-    
-    参数:
-        dir1: 第一个目录路径
-        dir2: 第二个目录路径
-    
-    返回:
-        DataFrame: 包含所有比较结果的DataFrame
-    """
-    # 收集两个目录的分数数据
-    def collect_scores(directory):
-        scores = defaultdict(lambda: {'total': 0, 'count': 0})
-        sub_scores = defaultdict(lambda: {'total': 0, 'count': 0})
-        
-        for category in os.listdir(directory):
-            category_path = os.path.join(directory, category)
-            if not os.path.isdir(category_path):
-                continue
-                
-            for subcategory in os.listdir(category_path):
-                subcategory_path = os.path.join(category_path, subcategory)
-                if not os.path.isdir(subcategory_path):
-                    continue
-                    
-                report_path = os.path.join(subcategory_path, 'report', 'full_image_comet.csv')
-                if os.path.exists(report_path):
-                    try:
-                        df = pd.read_csv(report_path)
-                        avg_score = df['avg_comet_sentence'].mean()
-                        # 更新大类统计
-                        scores[category]['total'] += avg_score
-                        scores[category]['count'] += 1
-                        # 更新子类统计
-                        sub_scores[(category, subcategory)]['total'] = avg_score
-                        sub_scores[(category, subcategory)]['count'] = 1
-                    except Exception as e:
-                        print(f"Error processing {report_path}: {e}")
-        
-        # 计算平均值
-        for category in scores:
-            if scores[category]['count'] > 0:
-                scores[category]['avg'] = scores[category]['total'] / scores[category]['count']
-        
-        for key in sub_scores:
-            if sub_scores[key]['count'] > 0:
-                sub_scores[key]['avg'] = sub_scores[key]['total'] / sub_scores[key]['count']
-        
-        return scores, sub_scores
-    
-    # 收集两个目录的数据
-    scores1, sub_scores1 = collect_scores(dir1)
-    scores2, sub_scores2 = collect_scores(dir2)
-    
-    # 准备结果数据
-    results = []
-    
-    # 比较大类
-    all_categories = set(scores1.keys()).union(set(scores2.keys()))
-    category_comparison = []
-    
-    for category in all_categories:
-        avg1 = scores1.get(category, {}).get('avg', 0)
-        avg2 = scores2.get(category, {}).get('avg', 0)
-        comparison = "dir1 > dir2" if avg1 > avg2 else ("dir1 < dir2" if avg1 < avg2 else "dir1 == dir2")
-        
-        category_comparison.append({
-            'category': category,
-            'type': 'category',
-            'dir1_avg': round(avg1, 3),
-            'dir2_avg': round(avg2, 3),
-            'comparison': comparison
-        })
-    
-    # 比较子类
-    all_subcategories = set(sub_scores1.keys()).union(set(sub_scores2.keys()))
-    subcategory_comparison = []
-    
-    for (category, subcategory) in all_subcategories:
-        avg1 = sub_scores1.get((category, subcategory), {}).get('avg', 0)
-        avg2 = sub_scores2.get((category, subcategory), {}).get('avg', 0)
-        comparison = "dir1 > dir2" if avg1 > avg2 else ("dir1 < dir2" if avg1 < avg2 else "dir1 == dir2")
-        
-        subcategory_comparison.append({
-            'category': category,
-            'type': 'subcategory',
-            'subcategory': subcategory,
-            'dir1_avg': round(avg1, 3),
-            'dir2_avg': round(avg2, 3),
-            'comparison': comparison
-        })
-    
-    # 合并结果
-    results = category_comparison + subcategory_comparison
-    
-    # 创建DataFrame
-    comparison_df = pd.DataFrame(results)
-    
-    # 总结性比较
-    total_categories = len(all_categories)
-    dir1_win_categories = sum(1 for c in category_comparison if c['comparison'] == 'dir1 > dir2')
-    dir2_win_categories = sum(1 for c in category_comparison if c['comparison'] == 'dir1 < dir2')
-    
-    total_subcategories = len(all_subcategories)
-    dir1_win_subcategories = sum(1 for c in subcategory_comparison if c['comparison'] == 'dir1 > dir2')
-    dir2_win_subcategories = sum(1 for c in subcategory_comparison if c['comparison'] == 'dir1 < dir2')
-    
-    # 打印总结
-    print("\n=== 总结性比较 ===")
-    print(f"大类总数: {total_categories}")
-    print(f"dir1获胜的大类数: {dir1_win_categories} ({dir1_win_categories/total_categories:.1%})")
-    print(f"dir2获胜的大类数: {dir2_win_categories} ({dir2_win_categories/total_categories:.1%})")
-    print(f"平局的大类数: {total_categories - dir1_win_categories - dir2_win_categories}")
-    
-    print(f"\n子类总数: {total_subcategories}")
-    print(f"dir1获胜的子类数: {dir1_win_subcategories} ({dir1_win_subcategories/total_subcategories:.1%})")
-    print(f"dir2获胜的子类数: {dir2_win_subcategories} ({dir2_win_subcategories/total_subcategories:.1%})")
-    print(f"平局的子类数: {total_subcategories - dir1_win_subcategories - dir2_win_subcategories}")
-    
-    # 保存结果
-    comparison_df.to_csv('category_score_comparison.csv', index=False)
-    print("\n比较结果已保存到 category_score_comparison.csv")
-    
-    return comparison_df
-if __name__ == "__main__":
-    print("Processing task 1...")
-    avg_scores = calculate_avg_comet('organized_data_1')
-    print("Average comet scores saved to comet_avg_scores.csv")
-    
-    print("\nProcessing task 2...")
-    win_rate_df = compare_win_rate('organized_data_1', 'organized_data_2')
-    print("\nWin rate comparison saved to win_rate_comparison.csv")

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