Weighted precision:0.8970199190787427
Weighted recall:0.8784313725490196
Weighted f1-score:0.8746117172587762
precision:0.8970199190787426
recall:0.8784313725490197
f1-score:0.8746117172587762
基于深度学习的作物病虫害识别的网络python源码+训练好的模型.zip （96个子文件） 
lib 
dataloader.pyc 1KB
aidemo.zip 6.46MB 说明.md 653B
CNLtrainlog.txt 42B train_val.py 19KB ipdemo.zip 18.46MB f1.txt 193B cnlnet_5CNL.pyc 13KB cupy_layers aggregation_zeropad.pyc 12KB aggregation_zeropad_dilate.pyc 13KB aggregation_zeropad_mix_merge.pyc 16KB __init__.pyc 138B utils.pyc 761B aggregation_zeropad_mix.pyc 17KB models psresnet.py 7KB __init__.py 1KB efficientnet.py 70KB efficientnet_blocks.py 14KB regnet.py 19KB layers weight_init.py 3KB __init__.py 2KB eca.py 5KB patch_embed.py 1KB mixed_conv2d.py 2KB activations_jit.py 2KB create_act.py 4KB conv2d_same.py 1KB separable_conv.py 3KB activations_me.py 5KB split_attn.py 3KB helpers.py 492B evo_norm.py 3KB norm_act.py 3KB drop.py 7KB padding.py 2KB median_pool.py 2KB space_to_depth.py 2KB activations.py 3KB blur_pool.py 2KB anti_aliasing.py 2KB selective_kernel.py 5KB shiftlution.py 3KB kerv2d.py 2KB pool2d_same.py 3KB split_batchnorm.py 3KB create_norm_act.py 3KB classifier.py 2KB conv_bn_act.py 1KB se.py 1KB std_conv.py 6KB inplace_abn.py 3KB mlp.py 4KB tbconv.py 4KB cond_conv2d.py 5KB create_conv2d.py 1KB create_attn.py 1KB cbam.py 3KB adaptive_avgmax_pool.py 4KB config.py 3KB lr_net.py 8KB tnt.py 11KB features.py 12KB visformer.py 16KB lambdAnet.py 6KB helpers.py 15KB levit.py 21KB xception.py 7KB swin_transformer.py 27KB resnetv2.py 27KB efficientnet_builder.py 17KB cnlnet_5CNL.pyc 13KB xcit.py 35KB cotnet.py 10KB rexnet.py 10KB cait.pyc 13KB botnet.pyc 10KB coat.pyc 20KB twins.py 17KB registry.py 4KB resnet_rs.py 13KB resnet_dw.py 4KB san_lowrank.py 9KB densenet.py 15KB resnet.py 53KB sknet.py 10KB factory.py 3KB cotnet_hybrid.py 21KB vision_transformer.py 42KB convit.py 13KB resnest.py 11KB vision_transformer_hybrid.py 16KB res2net.py 8KB pit.py 13KB config constants.pyc 585B __init__.pyc 266B config.pyc 4KB资源评论
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