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V0.3.0-Human3.6M-Weights
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@Lupin1998 Lupin1998 released this 19 Jun 21:52
human-weights
ec8bf72

We provide high-resolution benchmark results on Human3.6M dataset using $4\rightarrow 4$ frames prediction setting. Metrics (MSE, MAE, SSIM, pSNR, LPIPS) of the best models are reported in three trials. We use 256x256 resolutions, similar to STRPM. Parameters (M), FLOPs (G), and V100 inference FPS (s) are also reported for all methods. The default training setup is trained 100 epochs by Adam optimizer with a batch size of 16 and Cosine scheduler (no warm-up) on single GPU or 4GPUs, and we report the used GPU setups for each method (also shown in the config).

STL Benchmarks on Human 3.6M

Method Setting GPUs Params FLOPs FPS MSE MAE SSIM PSNR LPIPS Download
ConvLSTM-S 50 epoch 1xbs16 15.5M 347.0 52 125.5 1566.7 0.9813 33.40 0.03557 model | log
E3D-LSTM 50 epoch 4xbs4 60.9M 542.0 7 143.3 1442.5 0.9803 32.52 0.04133 model | log
PredNet 50 epoch 1xbs16 12.5M 13.7 176 261.9 1625.3 0.9786 31.76 0.03264 model | log
PhyDNet 50 epoch 1xbs16 4.2M 19.1 57 125.7 1614.7 0.9804 39.84 0.03709 model | log
MAU 50 epoch 1xbs16 20.2M 105.0 6 127.3 1577.0 0.9812 33.33 0.03561 model | log
MIM 50 epoch 4xbs4 47.6M 1051.0 17 112.1 1467.1 0.9829 33.97 0.03338 model | log
PredRNN 50 epoch 1xbs16 24.6M 704.0 25 113.2 1458.3 0.9831 33.94 0.03245 model | log
PredRNN++ 50 epoch 1xbs16 39.3M 1033.0 18 110.0 1452.2 0.9832 34.02 0.03196 model | log
PredRNN.V2 50 epoch 1xbs16 24.6M 708.0 24 114.9 1484.7 0.9827 33.84 0.03334 model | log
DMVFN 50 epoch 1xbs16 8.6M 63.6 341 109.3 1449.3 0.9833 34.05 0.03189 model | log
SimVP+IncepU 50 epoch 1xbs16 41.2M 197.0 26 115.8 1511.5 0.9822 33.73 0.03467 model | log
SimVP+gSTA-S 50 epoch 1xbs16 11.3M 74.6 52 108.4 1441.0 0.9834 34.08 0.03224 model | log
TAU 50 epoch 1xbs16 37.6M 182.0 26 113.3 1390.7 0.9839 34.03 0.02783 model | log

Benchmark of MetaFormers Based on SimVP (MetaVP)

MetaFormer Setting GPUs Params FLOPs FPS MSE MAE SSIM PSNR LPIPS Download
IncepU (SimVPv1) 50 epoch 1xbs16 41.2M 197.0 26 115.8 1511.5 0.9822 33.73 0.03467 model | log
gSTA (SimVPv2) 50 epoch 1xbs16 11.3M 74.6 52 108.4 1441.0 0.9834 34.08 0.03224 model | log
ViT 50 epoch 4xbs4 28.3M 239.0 17 136.3 1603.5 0.9796 33.10 0.03729 model | log
Swin Transformer 50 epoch 1xbs16 38.8M 188.0 28 133.2 1599.7 0.9799 33.16 0.03766 model | log
Uniformer 50 epoch 4xbs4 27.7M 211.0 14 116.3 1497.7 0.9824 33.76 0.03385 model | log
MLP-Mixer 50 epoch 1xbs16 47.0M 164.0 34 125.7 1511.9 0.9819 33.49 0.03417 model | log
ConvMixer 50 epoch 1xbs16 3.1M 39.4 84 115.8 1527.4 0.9822 33.67 0.03436 model | log
Poolformer 50 epoch 1xbs16 31.2M 156.0 30 118.4 1484.1 0.9827 33.78 0.03313 model | log
ConvNeXt 50 epoch 1xbs16 31.4M 157.0 33 113.4 1469.7 0.9828 33.86 0.03305 model | log
VAN 50 epoch 1xbs16 37.5M 182.0 24 111.4 1454.5 0.9831 33.93 0.03335 model | log
HorNet 50 epoch 1xbs16 28.1M 143.0 33 118.1 1481.1 0.9824 33.73 0.03333 model | log
MogaNet 50 epoch 1xbs16 8.6M 63.6 56 109.1 1446.4 0.9834 34.05 0.03163 model | log
TAU 50 epoch 1xbs16 37.6M 182.0 26 113.3 1390.7 0.9839 34.03 0.02783 model | log
Assets 48
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