docs: update metrics
gaotingquan
Tingquan Gao
parent
8b7b211bad
commit
898f12c308
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@ -354,24 +354,24 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
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|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------|
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| ViT_small_<br/>patch16_224 | 0.7769 | 0.9342 | 3.71 | 9.05 | 16.72 | 9.41 | 48.60 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_small_patch16_224_infer.tar) |
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| ViT_base_<br/>patch16_224 | 0.8195 | 0.9617 | 6.12 | 14.84 | 28.51 | 16.85 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_224_infer.tar) |
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| ViT_small_<br/>patch16_224 | 0.7553 | 0.9211 | 3.71 | 9.05 | 16.72 | 9.41 | 48.60 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_small_patch16_224_infer.tar) |
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| ViT_base_<br/>patch16_224 | 0.8187 | 0.9618 | 6.12 | 14.84 | 28.51 | 16.85 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_224_infer.tar) |
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| ViT_base_<br/>patch16_384 | 0.8414 | 0.9717 | 14.15 | 48.38 | 95.06 | 49.35 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch16_384_infer.tar) |
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| ViT_base_<br/>patch32_384 | 0.8176 | 0.9613 | 4.94 | 13.43 | 24.08 | 12.66 | 88.19 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_base_patch32_384_infer.tar) |
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| ViT_large_<br/>patch16_224 | 0.8323 | 0.9650 | 15.53 | 49.50 | 94.09 | 59.65 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_224_infer.tar) |
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| ViT_large_<br/>patch16_224 | 0.8303 | 0.9655 | 15.53 | 49.50 | 94.09 | 59.65 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_224_infer.tar) |
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|ViT_large_<br/>patch16_384| 0.8513 | 0.9736 | 39.51 | 152.46 | 304.06 | 174.70 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch16_384_infer.tar) |
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|ViT_large_<br/>patch32_384| 0.8153 | 0.9608 | 11.44 | 36.09 | 70.63 | 44.24 | 306.48 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ViT_large_patch32_384_infer.tar) |
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
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|------------------------|-----------|-----------|------------------|------------------|----------|------------------------|------------------------|------------------------|------------------------|
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| DeiT_tiny_<br>patch16_224 | 0.718 | 0.910 | 3.61 | 3.94 | 6.10 | 1.07 | 5.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_patch16_224_infer.tar) |
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| DeiT_small_<br>patch16_224 | 0.796 | 0.949 | 3.61 | 6.24 | 10.49 | 4.24 | 21.97 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_patch16_224_infer.tar) |
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| DeiT_base_<br>patch16_224 | 0.817 | 0.957 | 6.13 | 14.87 | 28.50 | 16.85 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_224_infer.tar) |
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| DeiT_base_<br>patch16_384 | 0.830 | 0.962 | 14.12 | 48.80 | 97.60 | 49.35 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_384_infer.tar) |
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| DeiT_tiny_<br>distilled_patch16_224 | 0.741 | 0.918 | 3.51 | 4.05 | 6.03 | 1.08 | 5.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_distilled_patch16_224_infer.tar) |
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| DeiT_small_<br>distilled_patch16_224 | 0.809 | 0.953 | 3.70 | 6.20 | 10.53 | 4.26 | 22.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_distilled_patch16_224_infer.tar) |
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| DeiT_base_<br>distilled_patch16_224 | 0.831 | 0.964 | 6.17 | 14.94 | 28.58 | 16.93 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_224_infer.tar) |
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| DeiT_base_<br>distilled_patch16_384 | 0.851 | 0.973 | 14.12 | 48.76 | 97.09 | 49.43 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_384_infer.tar) |
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| DeiT_tiny_<br>patch16_224 | 0.7208 | 0.9112 | 3.61 | 3.94 | 6.10 | 1.07 | 5.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_patch16_224_infer.tar) |
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| DeiT_small_<br>patch16_224 | 0.7982 | 0.9495 | 3.61 | 6.24 | 10.49 | 4.24 | 21.97 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_patch16_224_infer.tar) |
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| DeiT_base_<br>patch16_224 | 0.8180 | 0.9558 | 6.13 | 14.87 | 28.50 | 16.85 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_224_infer.tar) |
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| DeiT_base_<br>patch16_384 | 0.8289 | 0.9624 | 14.12 | 48.80 | 97.60 | 49.35 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_patch16_384_infer.tar) |
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| DeiT_tiny_<br>distilled_patch16_224 | 0.7449 | 0.9192 | 3.51 | 4.05 | 6.03 | 1.08 | 5.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_tiny_distilled_patch16_224_infer.tar) |
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| DeiT_small_<br>distilled_patch16_224 | 0.8117 | 0.9538 | 3.70 | 6.20 | 10.53 | 4.26 | 22.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_small_distilled_patch16_224_infer.tar) |
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| DeiT_base_<br>distilled_patch16_224 | 0.8330 | 0.9647 | 6.17 | 14.94 | 28.58 | 16.93 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_224_infer.tar) |
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| DeiT_base_<br>distilled_patch16_384 | 0.8520 | 0.9720 | 14.12 | 48.76 | 97.09 | 49.43 | 87.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DeiT_base_distilled_patch16_384_infer.tar) |
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<a name="RepVGG"></a>
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@ -426,14 +426,14 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
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| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | 6.59 | 9.68 | 16.32 | 4.35 | 28.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) |
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| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | 12.54 | 17.07 | 28.08 | 8.51 | 49.56 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) |
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| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
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| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
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| SwinTransformer_base_patch4_window7_224<sup>[1]</sup> | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
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| SwinTransformer_base_patch4_window12_384<sup>[1]</sup> | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
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| SwinTransformer_large_patch4_window7_224<sup>[1]</sup> | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_22kto1k_infer.tar) |
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| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_22kto1k_infer.tar) |
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| SwinTransformer_tiny_patch4_window7_224 | 0.8110 | 0.9549 | 6.59 | 9.68 | 16.32 | 4.35 | 28.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) |
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| SwinTransformer_small_patch4_window7_224 | 0.8321 | 0.9622 | 12.54 | 17.07 | 28.08 | 8.51 | 49.56 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) |
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| SwinTransformer_base_patch4_window7_224 | 0.8337 | 0.9643 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
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| SwinTransformer_base_patch4_window12_384 | 0.8417 | 0.9674 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
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| SwinTransformer_base_patch4_window7_224<sup>[1]</sup> | 0.8516 | 0.9748 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
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| SwinTransformer_base_patch4_window12_384<sup>[1]</sup> | 0.8634 | 0.9798 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
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| SwinTransformer_large_patch4_window7_224<sup>[1]</sup> | 0.8619 | 0.9788 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_22kto1k_infer.tar) |
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| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 0.8706 | 0.9814 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_22kto1k_infer.tar) |
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[1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
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@ -446,7 +446,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
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| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| LeViT_128S | 0.7598 | 0.9269 | | | | 281 | 7.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128S_infer.tar) |
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| LeViT_128 | 0.7810 | 0.9371 | | | | 365 | 8.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128_infer.tar) |
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| LeViT_128 | 0.7810 | 0.9372 | | | | 365 | 8.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128_infer.tar) |
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| LeViT_192 | 0.7934 | 0.9446 | | | | 597 | 10.61 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_192_infer.tar) |
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| LeViT_256 | 0.8085 | 0.9497 | | | | 1049 | 18.45 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_256_infer.tar) |
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| LeViT_384 | 0.8191 | 0.9551 | | | | 2234 | 38.45 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_384_infer.tar) |
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@ -461,12 +461,12 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
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| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| pcpvt_small | 0.8082 | 0.9552 | 7.32 | 10.51 | 15.27 |3.67 | 24.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_small_infer.tar) |
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| pcpvt_base | 0.8242 | 0.9619 | 12.20 | 16.22 | 23.16 | 6.44 | 43.83 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_base_infer.tar) |
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| pcpvt_large | 0.8273 | 0.9650 | 16.47 | 22.90 | 32.73 | 9.50 | 60.99 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_large_infer.tar) |
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| alt_gvt_small | 0.8140 | 0.9546 | 6.94 | 9.01 | 12.27 |2.81 | 24.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_small_infer.tar) |
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| alt_gvt_base | 0.8294 | 0.9621 | 9.37 | 15.02 | 24.54 | 8.34 | 56.07 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_base_infer.tar) |
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| alt_gvt_large | 0.8331 | 0.9642 | 11.76 | 22.08 | 35.12 | 14.81 | 99.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_large_infer.tar) |
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| pcpvt_small | 0.8115 | 0.9567 | 7.32 | 10.51 | 15.27 |3.67 | 24.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_small_infer.tar) |
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| pcpvt_base | 0.8268 | 0.9627 | 12.20 | 16.22 | 23.16 | 6.44 | 43.83 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_base_infer.tar) |
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| pcpvt_large | 0.8306 | 0.9659 | 16.47 | 22.90 | 32.73 | 9.50 | 60.99 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/pcpvt_large_infer.tar) |
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| alt_gvt_small | 0.8177 | 0.9557 | 6.94 | 9.01 | 12.27 |2.81 | 24.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_small_infer.tar) |
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| alt_gvt_base | 0.8315 | 0.9629 | 9.37 | 15.02 | 24.54 | 8.34 | 56.07 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_base_infer.tar) |
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| alt_gvt_large | 0.8364 | 0.9651 | 11.76 | 22.08 | 35.12 | 14.81 | 99.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/alt_gvt_large_infer.tar) |
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**注**：与 Reference 的精度差异源于数据预处理不同。
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@ -551,13 +551,13 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
|
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|
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|
||||
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
|
||||
| PVT_V2_B0 | 0.705 | 0.902 | - | - | - | 0.53 | 3.7 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B0_infer.tar) |
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| PVT_V2_B1 | 0.787 | 0.945 | - | - | - | 2.0 | 14.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B1_infer.tar) |
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| PVT_V2_B2 | 0.821 | 0.960 | - | - | - | 3.9 | 25.4 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B2_infer.tar) |
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| PVT_V2_B2_Linear | 0.821 | 0.961 | - | - | - | 3.8 | 22.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_Linear_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B2_Linear_infer.tar) |
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| PVT_V2_B3 | 0.831 | 0.965 | - | - |- | 6.7 | 45.2 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B3_infer.tar) |
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| PVT_V2_B4 | 0.836 | 0.967 | - | - | - | 9.8 | 62.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B4_infer.tar) |
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| PVT_V2_B5 | 0.837 | 0.966 | - | - | - | 11.4 | 82.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B5_infer.tar) |
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| PVT_V2_B0 | 0.7052 | 0.9016 | - | - | - | 0.53 | 3.7 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B0_infer.tar) |
|
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| PVT_V2_B1 | 0.7869 | 0.9450 | - | - | - | 2.0 | 14.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B1_infer.tar) |
|
||||
| PVT_V2_B2 | 0.8206 | 0.9599 | - | - | - | 3.9 | 25.4 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B2_infer.tar) |
|
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| PVT_V2_B2_Linear | 0.8205 | 0.9605 | - | - | - | 3.8 | 22.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_Linear_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B2_Linear_infer.tar) |
|
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| PVT_V2_B3 | 0.8310 | 0.9648 | - | - |- | 6.7 | 45.2 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B3_infer.tar) |
|
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| PVT_V2_B4 | 0.8361 | 0.9666 | - | - | - | 9.8 | 62.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B4_infer.tar) |
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| PVT_V2_B5 | 0.8374 | 0.9662 | - | - | - | 11.4 | 82.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B5_infer.tar) |
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<a name="MobileViT"></a>
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Loading…
Reference in New Issue