# PySlowFast Model Zoo and Baselines

## Kinetics 400 and 600

| 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`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/C2D_NOPOOL_8x8_R50.pkl) | Kinetics/c2/C2D_NOPOOL_8x8_R50 | K400 |
| I3D | R50 | 3 x 10 | 8 x 8 | 73.5 | 90.8 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/I3D_8x8_R50.pkl) | Kinetics/c2/I3D_8x8_R50 | K400 |
| I3D NLN | R50 | 3 x 10 | 8 x 8 | 74.0 | 91.1 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/I3D_NLN_8x8_R50.pkl) | Kinetics/c2/I3D_NLN_8x8_R50 | K400 |
| Slow | R50 | 3 x 10 | 4 x 16 | 72.7 | 90.3 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/SLOWONLY_4x16_R50.pkl) | Kinetics/c2/SLOW_4x16_R50 | K400 |
| Slow | R50 | 3 x 10 | 8 x 8 | 74.8 | 91.6 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/SLOWONLY_8x8_R50.pkl) | Kinetics/c2/SLOW_8x8_R50 | K400 |
| SlowFast | R50 | 3 x 10 | 4 x 16 | 75.6 | 92.0 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/SLOWFAST_4x16_R50.pkl) | Kinetics/c2/SLOWFAST_4x16_R50 | K400 |
| SlowFast | R50 | 3 x 10 | 8 x 8 | 77.0 | 92.6 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/SLOWFAST_8x8_R50.pkl) | Kinetics/c2/SLOWFAST_8x8_R50 | K400 |
| MViTv1 | B-Conv | 1 x 5 | 16 x 4 | 78.4 | 93.5 | [`link`](https://drive.google.com/file/d/194gJinVejq6A1FmySNKQ8vAN5-FOY-QL/view?usp=sharing) | Kinetics/MVIT_B_16x4_CONV | K400 |
| rev-MViT | B-Conv | 1 x 5 | 16 x 4 | 78.4 | 93.4 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/rev/REV_MVIT_B_16x4.pyth) | Kinetics/REV_MVIT_B_16x4_CONV | K400 |
| MViTv1 | B-Conv | 1 x 5 | 32 x 3 | 80.4 | 94.8 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/mvit/k400.pyth) | Kinetics/MVIT_B_32x3_CONV | K400 |
| MViTv1 | B-Conv | 1 x 5 | 32 x 3 | 83.9 | 96.5 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/mvit/k600.pyth) | Kinetics/MVIT_B_32x3_CONV_K600 | K600 |
| MViTv2 | S | 1 x 5 | 16 x 4 | 81.0 | 94.6 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/mvitv2/pysf_video_models/MViTv2_S_16x4_k400_f302660347.pyth) | Kinetics/MVITv2_S_16x4 | K400 |
| MViTv2 | B | 1 x 5 | 32 x 3 | 82.9 | 95.7 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/mvitv2/pysf_video_models/MViTv2_B_32x3_k400_f304025456.pyth) | Kinetics/MVITv2_B_32x3 | K400 |

## X3D models (details in projects/x3d)

|    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`](https://dl.fbaipublicfiles.com/pyslowfast/x3d_models/x3d_xs.pyth) | Kinetics/X3D_XS |
| X3D | S | - | 13 x 6 | 73.1 | 73.5 | 3.8 | 1.96 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/x3d_models/x3d_s.pyth) | Kinetics/X3D_S |
| X3D | M | - | 16 x 5 | 75.1 | 76.2 | 3.8 | 4.73 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/x3d_models/x3d_m.pyth) | Kinetics/X3D_M |
| X3D | L | - | 16 x 5 | 76.9 | 77.5 | 6.2 | 18.37 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/x3d_models/x3d_l.pyth) | Kinetics/X3D_L |

## AVA

| architecture | size | Pretrain Model |  frame length x sample rate  | MAP | AVA version | model |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |------------- |
| Slow | R50 | [Kinetics 400](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/pretrain/C2D_8x8_R50.pkl) | 4 x 16 | 19.5 | 2.2 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/C2D_8x8_R50.pkl) |
| SlowFast | R101 | [Kinetics 600](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/pretrain/SLOWFAST_32x2_R101_50_50_v2.1.pkl) | 8 x 8 | 28.2 | 2.1 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/SLOWFAST_32x2_R101_50_50_v2.1.pkl) |
| SlowFast | R101 | [Kinetics 600](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/pretrain/SLOWFAST_32x2_R101_50_50.pkl) | 8 x 8 | 29.1 | 2.2 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/SLOWFAST_32x2_R101_50_50.pkl) |
| SlowFast | R101 | [Kinetics 600](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/pretrain/SLOWFAST_64x2_R101_50_50.pkl) | 16 x 8 | 29.4 | 2.2 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/SLOWFAST_64x2_R101_50_50.pkl) |

## Multigrid Training

***Update June, 2020:*** In the following we provide (reimplemented) models from  "[A Multigrid Method for Efficiently Training Video Models
](https://arxiv.org/abs/1912.00998)" paper. The multigrid method trains about 3-6x faster than the original training on multiple datasets. See [projects/multigrid](projects/multigrid/README.md) for more information. The following provides models, results, and example config files.

#### Kinetics:
| architecture | size |  pretrain |  frame length x sample rate | training | top1 |  top5  |  model | config |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| SlowFast | R50 | - | 8 x 8 | Standard | 76.8 | 92.7 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/pyslowfast/model_zoo/multigrid/model_zoo/Kinetics/SLOWFAST_8x8_R50_stepwise.pkl) | Kinetics/SLOWFAST_8x8_R50_stepwise |
| SlowFast | R50 | - | 8 x 8 | Multigrid | 76.6 | 92.7 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/pyslowfast/model_zoo/multigrid/model_zoo/Kinetics/SLOWFAST_8x8_R50_stepwise_multigrid.pkl) | Kinetics/SLOWFAST_8x8_R50_stepwise_multigrid |

(Here we use stepwise learning rate schedule.)

#### Something-Something V2:
| architecture | size |  pretrain |  frame length x sample rate | training | top1 |  top5  |  model | config |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| SlowFast | R50 | [Kinetics 400](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/SLOWFAST_8x8_R50.pkl) | 16 x 8 | Standard | 63.0 | 88.5 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/pyslowfast/model_zoo/multigrid/model_zoo/SSv2/SLOWFAST_16x8_R50.pkl) | SSv2/SLOWFAST_16x8_R50 |
| SlowFast | R50 | [Kinetics 400](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/SLOWFAST_8x8_R50.pkl) | 16 x 8 | Multigrid | 63.5 | 88.7 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/pyslowfast/model_zoo/multigrid/model_zoo/SSv2/SLOWFAST_16x8_R50_multigrid.pkl) | SSv2/SLOWFAST_16x8_R50_multigrid |


#### Charades
| architecture | size |  pretrain |  frame length x sample rate | training | mAP |  model | config |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| SlowFast | R50 | [Kinetics 400](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/SLOWFAST_8x8_R50.pkl) | 16 x 8 | Standard | 38.9 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/pyslowfast/model_zoo/multigrid/model_zoo/Charades/SLOWFAST_16x8_R50.pkl) | SSv2/SLOWFAST_16x8_R50 |
| SlowFast | R50 | [Kinetics 400](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/SLOWFAST_8x8_R50.pkl) | 16 x 8 | Multigrid | 38.6 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/pyslowfast/model_zoo/multigrid/model_zoo/Charades/SLOWFAST_16x8_R50_multigrid.pkl) | SSv2/SLOWFAST_16x8_R50_multigrid |


## ImageNet

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`](https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/R50_IN1K.pyth) | ImageNet/RES_R50 |
| MVIT | B-16-Conv | 82.9 | 96.3 | [`link`](https://drive.google.com/file/d/1dYYqUB-3DSgBVc9d6o-rW8ojtVsrFLgp/view?usp=sharing) | ImageNet/MVIT_B_16_CONV |
| rev-VIT | Small | 79.9 | 94.9 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/rev/REV_VIT_S.pyth) | ImageNet/REV_VIT_S.yaml |
| rev-VIT | Base |  81.8 | 95.6 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/rev/REV_VIT_B.pyth) | ImageNet/REV_VIT_B.yaml |
| rev-MVIT | Base |  82.9* | 96.3 | [`link`](https://dl.fbaipublicfiles.com/pyslowfast/rev/REV_MVIT_B.pyth) | ImageNet/REV_MVIT_B_16_CONV.yaml |

*please refer to [Reversible Model Zoo](projects/rev/README.md).

## PyTorchVideo

We support and benchmark PyTorchVideo models and datasets in PySlowFast. See [projects/pytorchvideo](projects/pytorchvideo/README.md) for more information about PyTorchVideo Model Zoo.