植物分类-PlantsClassification-编程知识

植物分类-PlantsClassification

一、模型配置

一、backbone
resnet50
二、neck
GlobalAveragePooling
三、head
fc
四、loss
type=‘LabelSmoothLoss’,
label_smooth_val=0.1,
num_classes=30,
reduction=‘mean’,
loss_weight=1.0
五、optimizer
lr=0.1, momentum=0.9, type=‘SGD’, weight_decay=0.0001
六、scheduler
T_max=260, begin=20, by_epoch=True, end=300, type='CosineAnnealingLR
七、evaluator
topk=(1, 5 ), type=‘Accuracy’
八、max_epochs
300

九、Config

auto_scale_lr = dict(base_batch_size=256)
data_preprocessor = dict(mean=[123.675,116.28,103.53,],num_classes=30,std=[58.395,57.12,57.375,],to_rgb=True)
dataset_type = 'ImageNet'
data_root = 'data/PlantsClassification'
default_hooks = dict(checkpoint=dict(interval=1, type='CheckpointHook', max_keep_ckpts=2, save_best="auto"),logger=dict(interval=100, type='LoggerHook'),param_scheduler=dict(type='ParamSchedulerHook'),sampler_seed=dict(type='DistSamplerSeedHook'),timer=dict(type='IterTimerHook'),visualization=dict(enable=False, type='VisualizationHook'))
default_scope = 'mmpretrain'
env_cfg = dict(cudnn_benchmark=False,dist_cfg=dict(backend='nccl'),mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'none'
load_from = './work_dirs/resnet50_8xb32-coslr_in1k/resnet50_8xb32_in1k_20210831-ea4938fc.pth'
log_level = 'INFO'
model = dict(backbone=dict(depth=50,num_stages=4,out_indices=(3,),style='pytorch',type='ResNet'),head=dict(in_channels=2048,# loss=dict(loss_weight=1.0, type='CrossEntropyLoss'),loss=dict(type='LabelSmoothLoss',label_smooth_val=0.1,num_classes=30,reduction='mean',loss_weight=1.0),num_classes=30,topk=(1,5,),type='LinearClsHead'),data_preprocessor=data_preprocessor,neck=dict(type='GlobalAveragePooling'),type='ImageClassifier')
train_cfg = dict(by_epoch=True, max_epochs=300, val_interval=1)
optim_wrapper = dict(optimizer=dict(lr=0.1, momentum=0.9, type='SGD', weight_decay=0.0001))
param_scheduler = dict(T_max=260, begin=20, by_epoch=True, end=300, type='CosineAnnealingLR')
randomness = dict(deterministic=False, seed=None)
resume = False
test_cfg = dict()
test_pipeline = [dict(type='LoadImageFromFile'),dict(edge='short', scale=256, type='ResizeEdge'),dict(crop_size=224, type='CenterCrop'),dict(type='PackInputs'),
]
test_dataloader = dict(batch_size=32,collate_fn=dict(type='default_collate'),dataset=dict(data_root=data_root,pipeline=test_pipeline,split='test',ann_file='test.txt',type=dataset_type),num_workers=1,persistent_workers=True,pin_memory=True,sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(topk=(1,5,), type='Accuracy')train_pipeline = [dict(type='LoadImageFromFile'),dict(scale=224, type='RandomResizedCrop'),dict(direction='horizontal', prob=0.5, type='RandomFlip'),dict(type='PackInputs'),
]
train_dataloader = dict(batch_size=45,collate_fn=dict(type='default_collate'),dataset=dict(data_root=data_root,pipeline=train_pipeline,split='train',ann_file='train.txt',type=dataset_type),num_workers=1,persistent_workers=True,pin_memory=True,sampler=dict(shuffle=True, type='DefaultSampler'))val_cfg = dict()
val_dataloader = dict(batch_size=45,collate_fn=dict(type='default_collate'),dataset=dict(data_root=data_root,pipeline=test_pipeline,split='val',ann_file='val.txt',type=dataset_type),num_workers=1,persistent_workers=True,pin_memory=True,sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = test_evaluator
vis_backends = [dict(type='LocalVisBackend'),
]
visualizer = dict(type='UniversalVisualizer', vis_backends=[dict(type='LocalVisBackend'),])
work_dir = './work_dirs\\resnet50_8xb32-coslr_in1k'

二、训练结果

采用kaggle植物分类数据集，30分类，标签：
IMAGENET_CATEGORIES = [‘aloevera’, ‘banana’, ‘bilimbi’, ‘cantaloupe’, ‘cassava’, ‘coconut’, ‘corn’, ‘cucumber’,
‘curcuma’, ‘eggplant’, ‘galangal’, ‘ginger’, ‘guava’, ‘kale’, ‘longbeans’, ‘mango’, ‘melon’,
‘orange’, ‘paddy’, ‘papaya’, ‘peperchili’, ‘pineapple’, ‘pomelo’, ‘shallot’, ‘soybeans’,
‘spinach’, ‘sweetpotatoes’, ‘tobacco’, ‘waterapple’, ‘watermelon’]
“accuracy/top1”: 90.00000762939453, “accuracy/top5”: 98.0666732788086