更新
① Efficientnet_b8已经推出
python
>>import timm
>>model=timm.create_model('tf_efficientnet_b8',pretrained=False)
>>model
图像分类模型
[ResNext] ?
? [Aggregated Residual Transformations for Deep Neural Networks]
? [ResNext官方代码链接]
[Efficientnet]
? [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks]
? [EfficientNet官方代码链接]
基于PyTorch的图像分类模型
? [代码链接—pytorch-image-models]
? Python库—timm
timm的使用方法
安装timm包
pip install timm
测试
python
>>import timm
>>model=timm.create_model('resnet18',pretrained=False)
>>model
timm支持的模型列表
以下表格中是目前timm所支持的模型结构，可运行model=timm.create_model('模型名称')加载模型。
| 分类网络系列 |
|---|
| ig_resnext101_32x48d |
| tf_efficientnet_b8 |
| tf_efficientnet_b8_ap |
| tf_efficientnet_b7_ap |
| ig_resnext101_32x32d |
| tf_efficientnet_b7 |
| tf_efficientnet_b6_ap |
| swsl_resnext101_32x8d |
| tf_efficientnet_b5_ap |
| ig_resnext101_32x16d |
| tf_efficientnet_b6 |
| tf_efficientnet_b5 |
| swsl_resnext101_32x16d |
| tf_efficientnet_b4_ap |
| swsl_resnext101_32x4d |
| tf_efficientnet_b4 |
| pnasnet5large |
| ig_resnext101_32x8d |
| nasnetalarge |
| swsl_resnext50_32x4d |
| efficientnet_b3a |
| ssl_resnext101_32x16d |
| tf_efficientnet_b3_ap |
| tf_efficientnet_b3 |
| ssl_resnext101_32x8d |
| efficientnet_b3 |
| senet154 |
| gluon_senet154 |
| swsl_resnet50 |
| gluon_resnet152_v1s |
| ssl_resnext101_32x4d |
| gluon_seresnext101_32x4d |
| gluon_seresnext101_64x4d |
| efficientnet_b2a |
| gluon_resnext101_64x4d |
| mixnet_xl |
| gluon_resnet152_v1d |
| inception_resnet_v2 |
| tf_efficientnet_el |
| gluon_resnet101_v1d |
| efficientnet_b2 |
| gluon_resnext101_32x4d |
| ssl_resnext50_32x4d |
| gluon_resnet101_v1s |
| tf_efficientnet_b2_ap |
| seresnext101_32x4d |
| inception_v4 |
| dpn107 |
| tf_efficientnet_b2 |
| dpn92 |
| ens_adv_inception_resnet_v2 |
| gluon_seresnext50_32x4d |
| gluon_resnet152_v1c |
| dpn131 |
| gluon_resnet152_v1b |
| resnext50d_32x4d |
| dpn98 |
| gluon_xception65 |
| gluon_resnet101_v1c |
| hrnet_w64 |
| dla102x2 |
| gluon_resnext50_32x4d |
| resnext101_32x8d |
| tf_efficientnet_cc_b1_8e |
| gluon_resnet101_v1b |
| hrnet_w48 |
| tf_efficientnet_b1_ap |
| ssl_resnet50 |
| res2net50_26w_8s |
| res2net101_26w_4s |
| seresnext50_32x4d |
| gluon_resnet50_v1d |
| xception |
| resnet50 |
| mixnet_l |
| hrnet_w40 |
| hrnet_w44 |
| wide_resnet101_2 |
| tf_efficientnet_b1 |
| tf_mixnet_l |
| gluon_resnet50_v1s |
| tf_efficientnet_em |
| efficientnet_b1 |
| dla169 |
| seresnet152 |
| res2net50_26w_6s |
| resnext50_32x4d |
| dla102x |
| wide_resnet50_2 |
| dla60_res2net |
| hrnet_w32 |
| dla60_res2next |
| selecsls60b |
| seresnet101 |
| resnet152 |
| dla60x |
| res2next50 |
| hrnet_w30 |
| res2net50_14w_8s |
| dla102 |
| gluon_resnet50_v1c |
| seresnext26t_32x4d |
| seresnext26tn_32x4d |
| selecsls60 |
| res2net50_26w_4s |
| tf_efficientnet_cc_b0_8e |
| efficientnet_b0 |
| seresnet50 |
| tv_resnext50_32x4d |
| seresnext26d_32x4d |
| gluon_resnet50_v1b |
| res2net50_48w_2s |
| dpn68b |
| resnet101 |
| densenet161 |
| tf_efficientnet_cc_b0_4e |
| densenet201 |
| mixnet_m |
| tf_efficientnet_es |
| selecsls42b |
| seresnext26_32x4d |
| tf_efficientnet_b0_ap |
| dla60 |
| tf_mixnet_m |
| tf_efficientnet_b0 |
| hrnet_w18 |
| resnet26d |
| dpn68 |
| tv_resnet50 |
| mixnet_s |
| densenet169 |
| tf_mixnet_s |
| mobilenetv3_rw |
| tf_mobilenetv3_large_100 |
| semnasnet_100 |
| resnet26 |
| fbnetc_100 |
| hrnet_w18_small_v2 |
| resnet34 |
| seresnet34 |
| densenet121 |
| mnasnet_100 |
| dla34 |
| gluon_resnet34_v1b |
| spnasnet_100 |
| tf_mobilenetv3_large_075 |
| tv_resnet34 |
| swsl_resnet18 |
| ssl_resnet18 |
| hrnet_w18_small |
| tf_mobilenetv3_large_minimal_100 |
| seresnet18 |
| gluon_resnet18_v1b |
| resnet18 |
| tf_mobilenetv3_small_100 |
| dla60x_c |
| dla46x_c |
| tf_mobilenetv3_small_075 |
| dla46_c |
| tf_mobilenetv3_small_minimal_100 |
| tf_mixnet_l |
timm模型的训练结果
以下表格是timm官方公布的测试结果，我截取前30名在此展示，想要查看完整榜单请访问[results-imagenet.csv]。
| model | top1 | top1_err | top5 | top5_err | param_count | img_size | cropt_pct | interpolation |
|---|---|---|---|---|---|---|---|---|
| ig_resnext101_32x48d | 85.428 | 14.572 | 97.572 | 2.428 | 828.41 | 224 | 0.875 | bilinear |
| tf_efficientnet_b8 | 85.37 | 14.63 | 97.39 | 2.61 | 87.41 | 672 | 0.954 | bicubic |
| tf_efficientnet_b8_ap | 85.37 | 14.63 | 97.294 | 2.706 | 87.41 | 672 | 0.954 | bicubic |
| tf_efficientnet_b7_ap | 85.12 | 14.88 | 97.252 | 2.748 | 66.35 | 600 | 0.949 | bicubic |
| ig_resnext101_32x32d | 85.094 | 14.906 | 97.438 | 2.562 | 468.53 | 224 | 0.875 | bilinear |
| tf_efficientnet_b7 | 84.936 | 15.064 | 97.204 | 2.796 | 66.35 | 600 | 0.949 | bicubic |
| tf_efficientnet_b6_ap | 84.788 | 15.212 | 97.138 | 2.862 | 43.04 | 528 | 0.942 | bicubic |
| swsl_resnext101_32x8d | 84.284 | 15.716 | 97.176 | 2.824 | 88.79 | 224 | 0.875 | bilinear |
| tf_efficientnet_b5_ap | 84.252 | 15.748 | 96.974 | 3.026 | 30.39 | 456 | 0.934 | bicubic |
| ig_resnext101_32x16d | 84.17 | 15.83 | 97.196 | 2.804 | 194.03 | 224 | 0.875 | bilinear |
| tf_efficientnet_b6 | 84.11 | 15.89 | 96.886 | 3.114 | 43.04 | 528 | 0.942 | bicubic |
| tf_efficientnet_b5 | 83.812 | 16.188 | 96.748 | 3.252 | 30.39 | 456 | 0.934 | bicubic |
| swsl_resnext101_32x16d | 83.346 | 16.654 | 96.846 | 3.154 | 194.03 | 224 | 0.875 | bilinear |
| tf_efficientnet_b4_ap | 83.248 | 16.752 | 96.392 | 3.608 | 19.34 | 380 | 0.922 | bicubic |
| swsl_resnext101_32x4d | 83.23 | 16.77 | 96.76 | 3.24 | 44.18 | 224 | 0.875 | bilinear |
| tf_efficientnet_b4 | 83.022 | 16.978 | 96.3 | 3.7 | 19.34 | 380 | 0.922 | bicubic |
| pnasnet5large | 82.736 | 17.264 | 96.046 | 3.954 | 86.06 | 331 | 0.875 | bicubic |
| ig_resnext101_32x8d | 82.688 | 17.312 | 96.636 | 3.364 | 88.79 | 224 | 0.875 | bilinear |
| nasnetalarge | 82.554 | 17.446 | 96.038 | 3.962 | 88.75 | 331 | 0.875 | bicubic |
| swsl_resnext50_32x4d | 82.182 | 17.818 | 96.23 | 3.77 | 25.03 | 224 | 0.875 | bilinear |
| efficientnet_b3a | 81.866 | 18.134 | 95.836 | 4.164 | 12.23 | 320 | 1 | bicubic |
| ssl_resnext101_32x16d | 81.844 | 18.156 | 96.096 | 3.904 | 194.03 | 224 | 0.875 | bilinear |
| tf_efficientnet_b3_ap | 81.822 | 18.178 | 95.624 | 4.376 | 12.23 | 300 | 0.904 | bicubic |
| tf_efficientnet_b3 | 81.636 | 18.364 | 95.718 | 4.282 | 12.23 | 300 | 0.904 | bicubic |
| ssl_resnext101_32x8d | 81.616 | 18.384 | 96.038 | 3.962 | 88.79 | 224 | 0.875 | bilinear |
| efficientnet_b3 | 81.494 | 18.506 | 95.716 | 4.284 | 12.23 | 300 | 0.904 | bicubic |
| senet154 | 81.31 | 18.69 | 95.496 | 4.504 | 115.09 | 224 | 0.875 | bilinear |
| gluon_senet154 | 81.234 | 18.766 | 95.348 | 4.652 | 115.09 | 224 | 0.875 | bicubic |
| swsl_resnet50 | 81.166 | 18.834 | 95.972 | 4.028 | 25.56 | 224 | 0.875 | bilinear |
另外
图像分类大赛
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