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V0.3.0-KITTICaltech-Weights
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@Lupin1998 Lupin1998 released this 18 Jun 23:18
kitti-weights
f09ce01

We provide benchmark results on KittiCaltech Pedestrian dataset using $10\rightarrow 1$ frames prediction setting following PredNet. Metrics (MSE, MAE, SSIM, pSNR, LPIPS) of the best models are reported in three trials. 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 Onecycle scheduler on single GPU, while some computational consuming methods (denoted by *) using 4GPUs.

STL Benchmarks on KittiCaltech

Method Setting Params FLOPs FPS MSE MAE SSIM PSNR LPIPS Download
ConvLSTM-S 100 epoch 15.0M 595.0G 33 139.6 1583.3 0.9345 27.46 0.08575 model | log
E3D-LSTM* 100 epoch 54.9M 1004G 10 200.6 1946.2 0.9047 25.45 0.12602 model | log
PredNet 100 epoch 12.5M 42.8G 94 159.8 1568.9 0.9286 27.21 0.11289 model | log
PhyDNet 100 epoch 3.1M 40.4G 117 312.2 2754.8 0.8615 23.26 0.32194 model | log
MAU 100 epoch 24.3M 172.0G 16 177.8 1800.4 0.9176 26.14 0.09673 model | log
MIM 100 epoch 49.2M 1858G 39 125.1 1464.0 0.9409 28.10 0.06353 model | log
PredRNN 100 epoch 23.7M 1216G 17 130.4 1525.5 0.9374 27.81 0.07395 model | log
PredRNN++ 100 epoch 38.5M 1803G 12 125.5 1453.2 0.9433 28.02 0.13210 model | log
PredRNN.V2 100 epoch 23.8M 1223G 52 147.8 1610.5 0.9330 27.12 0.08920 model | log
DMVFN 100 epoch 3.6M 1.2G 557 183.9 1531.1 0.9314 26.95 0.04942 model | log
SimVP+IncepU 100 epoch 8.6M 60.6G 57 160.2 1690.8 0.9338 26.81 0.06755 model | log
SimVP+gSTA-S 100 epoch 15.6M 96.3G 40 129.7 1507.7 0.9454 27.89 0.05736 model | log
TAU 100 epoch 44.7M 80.0G 55 131.1 1507.8 0.9456 27.83 0.05494 model | log

Benchmark of MetaFormers Based on SimVP (MetaVP)

MetaFormer Setting Params FLOPs FPS MSE MAE SSIM PSNR LPIPS Download
IncepU (SimVPv1) 100 epoch 8.6M 60.6G 57 160.2 1690.8 0.9338 26.81 0.06755 model | log
gSTA (SimVPv2) 100 epoch 15.6M 96.3G 40 129.7 1507.7 0.9454 27.89 0.05736 model | log
ViT* 100 epoch 12.7M 155.0G 25 146.4 1615.8 0.9379 27.43 0.06659 model | log
Swin Transformer 100 epoch 15.3M 95.2G 49 155.2 1588.9 0.9299 27.25 0.08113 model | log
Uniformer* 100 epoch 11.8M 104.0G 28 135.9 1534.2 0.9393 27.66 0.06867 model | log
MLP-Mixer 100 epoch 22.2M 83.5G 60 207.9 1835.9 0.9133 26.29 0.07750 model | log
ConvMixer 100 epoch 1.5M 23.1G 129 174.7 1854.3 0.9232 26.23 0.07758 model | log
Poolformer 100 epoch 12.4M 79.8G 51 153.4 1613.5 0.9334 27.38 0.07000 model | log
ConvNeXt 100 epoch 12.5M 80.2G 54 146.8 1630.0 0.9336 27.19 0.06987 model | log
VAN 100 epoch 14.9M 92.5G 41 127.5 1476.5 0.9462 27.98 0.05500 model | log
HorNet 100 epoch 15.3M 94.4G 43 152.8 1637.9 0.9365 27.09 0.06004 model | log
MogaNet 100 epoch 15.6M 96.2G 36 131.4 1512.1 0.9442 27.79 0.05394 model | log
TAU 100 epoch 44.7M 80.0G 55 131.1 1507.8 0.9456 27.83 0.05494 model | log
Assets 48
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