YOLO扑克牌检测数据集(极高精度)1数据集详情检测类别10 Diamonds10 Hearts10 Spades10 Trefoils2 Diamonds2 Hearts2 Spades2 Trefoils3 Diamonds3 Hearts3 Spades3 Trefoils4 Diamonds4 Hearts4 Spades4 Trefoils5 Diamonds5 Hearts5 Spades5 Trefoils596 Diamonds6 Hearts6 Spades6 Trefoils7 Diamonds7 Hearts7 Spades7 Trefoils8 Diamonds8 Hearts8 Spades8 Trefoils9 Diamonds9 Hearts9 Spades9 TrefoilsA DiamondsA HeartsA SpadesA TrefoilsJ DiamondsJ HeartsJ SpadesJ TrefoilsK DiamondsK HeartsK SpadesK TrefoilsQ DiamondsQ HeartsQ SpadesQ Trefoils图片总量1285张训练集964张验证集193张测试集128张标注状态全部已标注即拿即用数据格式支持YOLO格式及其他格式可按需提供YOLO扑克牌检测数据集一、数据集信息表项目详细说明数据集名称YOLO扑克牌检测数据集高精度图片总数量1285 张数据集划分训练集964 张验证集193 张测试集128 张检测类别共53类10 Diamonds、10 Hearts、10 Spades、10 Trefoils、2 Diamonds、2 Hearts、2 Spades、2 Trefoils、3 Diamonds、3 Hearts、3 Spades、3 Trefoils、4 Diamonds、4 Hearts、4 Spades、4 Trefoils、5 Diamonds、5 Hearts、5 Spades、5 Trefoils、59、6 Diamonds、6 Hearts、6 Spades、6 Trefoils、7 Diamonds、7 Hearts、7 Spades、7 Trefoils、8 Diamonds、8 Hearts、8 Spades、8 Trefoils、9 Diamonds、9 Hearts、9 Spades、9 Trefoils、A Diamonds、A Hearts、A Spades、A Trefoils、J Diamonds、J Hearts、J Spades、J Trefoils、K Diamonds、K Hearts、K Spades、K Trefoils、Q Diamonds、Q Hearts、Q Spades、Q Trefoils标注状态全量完成标注开箱即用数据格式标准YOLO格式同时支持多种主流标注格式可按需转换数据集特点数据质量高训练后模型可达到极高检测精度适用场景扑克识别、棋牌视觉检测、目标检测算法训练、计算机视觉实训、项目开发、毕业设计二、核心标签#扑克牌检测 #YOLO数据集 #棋牌识别 #目标检测 #AI视觉训练数据集三、数据集目录结构poker_dataset/ ├── images/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels/ │ ├── train/ │ ├── val/ │ └── test/ └── poker.yaml四、数据集配置文件poker.yamlpath:./poker_datasettrain:images/trainval:images/valtest:images/testnc:53names:0:10 Diamonds1:10 Hearts2:10 Spades3:10 Trefoils4:2 Diamonds5:2 Hearts6:2 Spades7:2 Trefoils8:3 Diamonds9:3 Hearts10:3 Spades11:3 Trefoils12:4 Diamonds13:4 Hearts14:4 Spades15:4 Trefoils16:5 Diamonds17:5 Hearts18:5 Spades19:5 Trefoils20:5921:6 Diamonds22:6 Hearts23:6 Spades24:6 Trefoils25:7 Diamonds26:7 Hearts27:7 Spades28:7 Trefoils29:8 Diamonds30:8 Hearts31:8 Spades32:8 Trefoils33:9 Diamonds34:9 Hearts35:9 Spades36:9 Trefoils37:A Diamonds38:A Hearts39:A Spades40:A Trefoils41:J Diamonds42:J Hearts43:J Spades44:J Trefoils45:K Diamonds46:K Hearts47:K Spades48:K Trefoils49:Q Diamonds50:Q Hearts51:Q Spades52:Q Trefoils五、环境依赖安装pipinstallultralytics torch opencv-python六、模型训练代码train_poker.pyfromultralyticsimportYOLOdefmain():# 加载预训练模型支持 YOLOv8 / YOLOv11 等版本modelYOLO(yolov8s.pt)# 启动训练train_resultsmodel.train(data./poker_dataset/poker.yaml,epochs120,imgsz640,batch16,device0,# 无GPU则改为 devicecpuworkers4,patience20,projectruns/train,namepoker_detect,exist_okTrue,pretrainedTrue)print(训练完成权重保存路径runs/train/poker_detect/weights)# 测试集评估metricsmodel.val(data./poker_dataset/poker.yaml,splittest)print(f测试集 mAP50:{metrics.box.map50:.4f})if__name____main__:main()七、推理测试代码predict_poker.pyfromultralyticsimportYOLOimportcv2# 加载训练完成的最优权重modelYOLO(runs/train/poker_detect/weights/best.pt)if__name____main__:img_pathtest_poker.jpg# 执行检测并保存结果resultsmodel.predict(sourceimg_path,conf0.25,iou0.45,saveTrue)# 可视化展示result_imgresults[0].plot()cv2.imshow(Poker Detection,result_img)cv2.waitKey(0)cv2.destroyAllWindows()