基于ImageNet1k分类数据集,PaddleClas支持的36种系列分类网络结构以及对应的175个图像分类预训练模型如下所示,训练技巧、每个系列网络结构的简单介绍和性能评估将在相应章节展现。
- CPU的评估环境基于骁龙855(SD855)。
- Intel CPU的评估环境基于Intel(R) Xeon(R) Gold 6148。
- GPU评估环境基于V100和TensorRT。
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ResNet及其Vd系列
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轻量级模型系列
- PP-LCNet系列[28](论文地址)
- MobileNetV3系列[3](论文地址)
- MobileNetV3_large_x0_35
- MobileNetV3_large_x0_5
- MobileNetV3_large_x0_75
- MobileNetV3_large_x1_0
- MobileNetV3_large_x1_25
- MobileNetV3_small_x0_35
- MobileNetV3_small_x0_5
- MobileNetV3_small_x0_75
- MobileNetV3_small_x1_0
- MobileNetV3_small_x1_25
- MobileNetV3_large_x1_0_ssld
- MobileNetV3_large_x1_0_ssld_int8(coming soon)
- MobileNetV3_small_x1_0_ssld
- MobileNetV2系列[4](论文地址)
- MobileNetV1系列[5](论文地址)
- ShuffleNetV2系列[6](论文地址)
- GhostNet系列[23](论文地址)
- MixNet系列[29](论文地址)
- ReXNet系列[30](论文地址)
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SEResNeXt与Res2Net系列
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Inception系列
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HRNet系列
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DPN与DenseNet系列
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EfficientNet与ResNeXt101_wsl系列
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ResNeSt与RegNet系列
- ResNeSt系列[24](论文地址)
- RegNet系列[25](paper link)
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Transformer系列
- Swin-transformer系列[27](论文地址)
- SwinTransformer_tiny_patch4_window7_224
- SwinTransformer_small_patch4_window7_224
- SwinTransformer_base_patch4_window7_224
- SwinTransformer_base_patch4_window12_384
- SwinTransformer_base_patch4_window7_224_22k
- SwinTransformer_base_patch4_window7_224_22kto1k
- SwinTransformer_large_patch4_window12_384_22k
- SwinTransformer_large_patch4_window12_384_22kto1k
- SwinTransformer_large_patch4_window7_224_22k
- SwinTransformer_large_patch4_window7_224_22kto1k
- ViT系列[31](论文地址)
- DeiT系列[32](论文地址)
- LeViT系列[33](论文地址)
- Twins系列[34](论文地址)
- TNT系列[35](论文地址)
- Swin-transformer系列[27](论文地址)
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其他模型
注意:以上模型中EfficientNetB1-B7的预训练模型转自pytorch版EfficientNet,ResNeXt101_wsl系列预训练模型转自官方repo,剩余预训练模型均基于飞桨训练得到的,并在configs里给出了相应的训练超参数。
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