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PySlowFast Model Zoo and Baselines

Kinetics

We provided original pretrained models from Caffe2 on heavy models (testing Caffe2 pretrained model in PyTorch might have a small different in performance):

architecture depth pretrain frame length x sample rate top1 top5 model config
C2D R50 Train From Scratch 8 x 8 67.2 87.8 link Kinetics/c2/C2D_NOPOOL_8x8_R50
I3D R50 Train From Scratch 8 x 8 73.5 90.8 link Kinetics/c2/I3D_8x8_R50
I3D NLN R50 Train From Scratch 8 x 8 74.0 91.1 link Kinetics/c2/I3D_NLN_8x8_R50
SlowOnly R50 Train From Scratch 4 x 16 72.7 90.3 link Kinetics/c2/SLOWONLY_4x16_R50
SlowOnly R50 Train From Scratch 8 x 8 74.8 91.6 link Kinetics/c2/SLOWONLY_8x8_R50
SlowFast R50 Train From Scratch 4 x 16 75.6 92.0 link Kinetics/c2/SLOWFAST_4x16_R50
SlowFast R50 Train From Scratch 8 x 8 77.0 92.6 link Kinetics/c2/SLOWFAST_8x8_R50
SlowFast R101 Train From Scratch 8 x 8 78.0 93.3 link Kinetics/c2/SLOWFAST_8x8_R101_101_101
SlowFast R101 Train From Scratch 16 x 8 78.9 93.5 link Kinetics/c2/SLOWFAST_16x8_R101_50_50

AVA

architecture depth Pretrain Model frame length x sample rate MAP AVA version model
SlowOnly R50 Kinetics 400 4 x 16 19.5 2.2 link
SlowFast R101 Kinetics 600 8 x 8 28.2 2.1 link
SlowFast R101 Kinetics 600 8 x 8 29.1 2.2 link
SlowFast R101 Kinetics 600 16 x 8 29.4 2.2 link

ImageNet

We also release the imagenet pretrain model if finetune from ImageNet pretrain is preferred. The reported accuracy is obtrained by center crop testing on validation set.

architecture depth Top1 Top5 model
ResNet R50 23.6 6.8 link