| architecture | size | crops x clips | frame length x sample rate | top1 | top5 | model | config | dataset |
|---|
| C2D | R50 | 3 x 10 | 8 x 8 | 67.2 | 87.8 | link | Kinetics/c2/C2D_NOPOOL_8x8_R50 | K400 |
| I3D | R50 | 3 x 10 | 8 x 8 | 73.5 | 90.8 | link | Kinetics/c2/I3D_8x8_R50 | K400 |
| I3D NLN | R50 | 3 x 10 | 8 x 8 | 74.0 | 91.1 | link | Kinetics/c2/I3D_NLN_8x8_R50 | K400 |
| Slow | R50 | 3 x 10 | 4 x 16 | 72.7 | 90.3 | link | Kinetics/c2/SLOW_4x16_R50 | K400 |
| Slow | R50 | 3 x 10 | 8 x 8 | 74.8 | 91.6 | link | Kinetics/c2/SLOW_8x8_R50 | K400 |
| SlowFast | R50 | 3 x 10 | 4 x 16 | 75.6 | 92.0 | link | Kinetics/c2/SLOWFAST_4x16_R50 | K400 |
| SlowFast | R50 | 3 x 10 | 8 x 8 | 77.0 | 92.6 | link | Kinetics/c2/SLOWFAST_8x8_R50 | K400 |
| MViTv1 | B-Conv | 1 x 5 | 16 x 4 | 78.4 | 93.5 | link | Kinetics/MVIT_B_16x4_CONV | K400 |
| rev-MViT | B-Conv | 1 x 5 | 16 x 4 | 78.4 | 93.4 | link | Kinetics/REV_MVIT_B_16x4_CONV | K400 |
| MViTv1 | B-Conv | 1 x 5 | 32 x 3 | 80.4 | 94.8 | link | Kinetics/MVIT_B_32x3_CONV | K400 |
| MViTv1 | B-Conv | 1 x 5 | 32 x 3 | 83.9 | 96.5 | link | Kinetics/MVIT_B_32x3_CONV_K600 | K600 |
| MViTv2 | S | 1 x 5 | 16 x 4 | 81.0 | 94.6 | link | Kinetics/MVITv2_S_16x4 | K400 |
| MViTv2 | B | 1 x 5 | 32 x 3 | 82.9 | 95.7 | link | Kinetics/MVITv2_B_32x3 | K400 |
| architecture | size | pretrain | frame length x sample rate | top1 10-view | top1 30-view | parameters (M) | FLOPs (G) | model | config |
|---|
| X3D | XS | - | 4 x 12 | 68.7 | 69.5 | 3.8 | 0.60 | link | Kinetics/X3D_XS |
| X3D | S | - | 13 x 6 | 73.1 | 73.5 | 3.8 | 1.96 | link | Kinetics/X3D_S |
| X3D | M | - | 16 x 5 | 75.1 | 76.2 | 3.8 | 4.73 | link | Kinetics/X3D_M |
| X3D | L | - | 16 x 5 | 76.9 | 77.5 | 6.2 | 18.37 | link | Kinetics/X3D_L |
Update June, 2020: In the following we provide (reimplemented) models from "A Multigrid Method for Efficiently Training Video Models
" paper. The multigrid method trains about 3-6x faster than the original training on multiple datasets. See projects/multigrid for more information. The following provides models, results, and example config files.
| architecture | size | pretrain | frame length x sample rate | training | top1 | top5 | model | config |
|---|
| SlowFast | R50 | - | 8 x 8 | Standard | 76.8 | 92.7 | link | Kinetics/SLOWFAST_8x8_R50_stepwise |
| SlowFast | R50 | - | 8 x 8 | Multigrid | 76.6 | 92.7 | link | Kinetics/SLOWFAST_8x8_R50_stepwise_multigrid |
(Here we use stepwise learning rate schedule.)
| architecture | size | pretrain | frame length x sample rate | training | top1 | top5 | model | config |
|---|
| SlowFast | R50 | Kinetics 400 | 16 x 8 | Standard | 63.0 | 88.5 | link | SSv2/SLOWFAST_16x8_R50 |
| SlowFast | R50 | Kinetics 400 | 16 x 8 | Multigrid | 63.5 | 88.7 | link | SSv2/SLOWFAST_16x8_R50_multigrid |
| architecture | size | pretrain | frame length x sample rate | training | mAP | model | config |
|---|
| SlowFast | R50 | Kinetics 400 | 16 x 8 | Standard | 38.9 | link | SSv2/SLOWFAST_16x8_R50 |
| SlowFast | R50 | Kinetics 400 | 16 x 8 | Multigrid | 38.6 | link | SSv2/SLOWFAST_16x8_R50_multigrid |
We also release the imagenet pretrained model if finetuning from ImageNet is preferred. The reported accuracy is obtained by center crop testing on the validation set.
| architecture | size | Top1 | Top5 | model | Config |
|---|
| ResNet | R50 | 76.4 | 93.2 | link | ImageNet/RES_R50 |
| MVIT | B-16-Conv | 82.9 | 96.3 | link | ImageNet/MVIT_B_16_CONV |
| rev-VIT | Small | 79.9 | 94.9 | link | ImageNet/REV_VIT_S.yaml |
| rev-VIT | Base | 81.8 | 95.6 | link | ImageNet/REV_VIT_B.yaml |
| rev-MVIT | Base | 82.9* | 96.3 | link | ImageNet/REV_MVIT_B_16_CONV.yaml |
*please refer to Reversible Model Zoo.
We support and benchmark PyTorchVideo models and datasets in PySlowFast. See projects/pytorchvideo for more information about PyTorchVideo Model Zoo.