深圳网站制作的公司,设计企业网络方案的五个步骤,x域名免费网站,上海市建设小学网站一、分类原因
由于植物分类所使用的数据集存在一定问题#xff0c;修改起来比较麻烦#xff0c;本次采用kaggle的ButterflyMothsImageClassification数据集#xff0c;对100这种蝴蝶进行分类。
二、100中蝴蝶类别
‘ADONIS’,‘AFRICAN GIANT SWALLOWTAIL’,‘AMERICAN S…一、分类原因
由于植物分类所使用的数据集存在一定问题修改起来比较麻烦本次采用kaggle的ButterflyMothsImageClassification数据集对100这种蝴蝶进行分类。
二、100中蝴蝶类别
‘ADONIS’,‘AFRICAN GIANT SWALLOWTAIL’,‘AMERICAN SNOOT’,‘AN 88’,‘APPOLLO’,‘ARCIGERA FLOWER MOTH’,‘ATALA’,‘ATLAS MOTH’,‘BANDED ORANGE HELICONIAN’,‘BANDED PEACOCK’,‘BANDED TIGER MOTH’,‘BECKERS WHITE’,‘BIRD CHERRY ERMINE MOTH’,‘BLACK HAIRSTREAK’,‘BLUE MORPHO’,‘BLUE SPOTTED CROW’,‘BROOKES BIRDWING’,‘BROWN ARGUS’,‘BROWN SIPROETA’,‘CABBAGE WHITE’,‘CAIRNS BIRDWING’,‘CHALK HILL BLUE’,‘CHECQUERED SKIPPER’,‘CHESTNUT’,‘CINNABAR MOTH’,‘CLEARWING MOTH’,‘CLEOPATRA’,‘CLODIUS PARNASSIAN’,‘CLOUDED SULPHUR’,‘COMET MOTH’,‘COMMON BANDED AWL’,‘COMMON WOOD-NYMPH’,‘COPPER TAIL’,‘CRECENT’,‘CRIMSON PATCH’,‘DANAID EGGFLY’,‘EASTERN COMA’,‘EASTERN DAPPLE WHITE’,‘EASTERN PINE ELFIN’,‘ELBOWED PIERROT’,‘EMPEROR GUM MOTH’,‘GARDEN TIGER MOTH’,‘GIANT LEOPARD MOTH’,‘GLITTERING SAPPHIRE’,‘GOLD BANDED’,‘GREAT EGGFLY’,‘GREAT JAY’,‘GREEN CELLED CATTLEHEART’,‘GREEN HAIRSTREAK’,‘GREY HAIRSTREAK’,‘HERCULES MOTH’,‘HUMMING BIRD HAWK MOTH’,‘INDRA SWALLOW’,‘IO MOTH’,‘Iphiclus sister’,‘JULIA’,‘LARGE MARBLE’,‘LUNA MOTH’,‘MADAGASCAN SUNSET MOTH’,‘MALACHITE’,‘MANGROVE SKIPPER’,‘MESTRA’,‘METALMARK’,‘MILBERTS TORTOISESHELL’,‘MONARCH’,‘MOURNING CLOAK’,‘OLEANDER HAWK MOTH’,‘ORANGE OAKLEAF’,‘ORANGE TIP’,‘ORCHARD SWALLOW’,‘PAINTED LADY’,‘PAPER KITE’,‘PEACOCK’,‘PINE WHITE’,‘PIPEVINE SWALLOW’,‘POLYPHEMUS MOTH’,‘POPINJAY’,‘PURPLE HAIRSTREAK’,‘PURPLISH COPPER’,‘QUESTION MARK’,‘RED ADMIRAL’,‘RED CRACKER’,‘RED POSTMAN’,‘RED SPOTTED PURPLE’,‘ROSY MAPLE MOTH’,‘SCARCE SWALLOW’,‘SILVER SPOT SKIPPER’,‘SIXSPOT BURNET MOTH’,‘SLEEPY ORANGE’,‘SOOTYWING’,‘SOUTHERN DOGFACE’,‘STRAITED QUEEN’,‘TROPICAL LEAFWING’,‘TWO BARRED FLASHER’,‘ULYSES’,‘VICEROY’,‘WHITE LINED SPHINX MOTH’,‘WOOD SATYR’,‘YELLOW SWALLOW TAIL’,‘ZEBRA LONG WING’
三、配置文件
auto_scale_lr dict(base_batch_size256)
data_preprocessor dict(mean[123.675,116.28,103.53,],num_classes100,std[58.395,57.12,57.375,],to_rgbTrue)
dataset_type ImageNet
data_root data/ButterflyMothsImageClassification
default_hooks dict(checkpointdict(interval1, typeCheckpointHook, max_keep_ckpts2, save_bestauto),loggerdict(interval100, typeLoggerHook),param_schedulerdict(typeParamSchedulerHook),sampler_seeddict(typeDistSamplerSeedHook),timerdict(typeIterTimerHook),visualizationdict(enableFalse, typeVisualizationHook))
default_scope mmpretrain
env_cfg dict(cudnn_benchmarkFalse,dist_cfgdict(backendnccl),mp_cfgdict(mp_start_methodfork, opencv_num_threads0))
launcher none
load_from ./work_dirs/resnet50_8xb32-coslr_in1k/resnet50_8xb32_in1k_20210831-ea4938fc.pth
log_level INFO
model dict(backbonedict(depth50,num_stages4,out_indices(3,),stylepytorch,typeResNet),headdict(in_channels2048,# lossdict(loss_weight1.0, typeCrossEntropyLoss),lossdict(typeLabelSmoothLoss,label_smooth_val0.1,num_classes100,reductionmean,loss_weight1.0),num_classes100,topk(1,5,),typeLinearClsHead),data_preprocessordata_preprocessor,neckdict(typeGlobalAveragePooling),typeImageClassifier)
train_cfg dict(by_epochTrue, max_epochs300, val_interval1)
optim_wrapper dict(optimizerdict(lr0.1, momentum0.9, typeSGD, weight_decay0.0001))
param_scheduler dict(T_max260, begin20, by_epochTrue, end300, typeCosineAnnealingLR)
randomness dict(deterministicFalse, seedNone)
resume False
test_cfg dict()
test_pipeline [dict(typeLoadImageFromFile),dict(edgeshort, scale256, typeResizeEdge),dict(crop_size224, typeCenterCrop),dict(typePackInputs),
]
test_dataloader dict(batch_size32,collate_fndict(typedefault_collate),datasetdict(data_rootdata_root,pipelinetest_pipeline,splittest,ann_filetest.txt,typedataset_type),num_workers1,persistent_workersTrue,pin_memoryTrue,samplerdict(shuffleFalse, typeDefaultSampler))
test_evaluator dict(topk(1,5,), typeAccuracy)train_pipeline [dict(typeLoadImageFromFile),dict(scale224, typeRandomResizedCrop),dict(directionhorizontal, prob0.5, typeRandomFlip),dict(typePackInputs),
]
train_dataloader dict(batch_size45,collate_fndict(typedefault_collate),datasetdict(data_rootdata_root,pipelinetrain_pipeline,splittrain,ann_filetrain.txt,typedataset_type),num_workers1,persistent_workersTrue,pin_memoryTrue,samplerdict(shuffleTrue, typeDefaultSampler))val_cfg dict()
val_dataloader dict(batch_size45,collate_fndict(typedefault_collate),datasetdict(data_rootdata_root,pipelinetest_pipeline,splitval,ann_filevalid.txt,typedataset_type),num_workers1,persistent_workersTrue,pin_memoryTrue,samplerdict(shuffleFalse, typeDefaultSampler))
val_evaluator test_evaluator
vis_backends [dict(typeLocalVisBackend),
]
visualizer dict(typeUniversalVisualizer, vis_backends[dict(typeLocalVisBackend),])
work_dir ./work_dirs\\resnet50_8xb32-coslr_in1k
三、训练结果
accuracy/top1: 97.0000 accuracy/top5: 99.0000
四、结果展示
