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```

## Toy Examples
To try the examples, you may download `hmdb6` dataset, which is a subset of `hmdb51` with only 6 classes. All the datasets can be downloaded from [Google Drive](https://drive.google.com/drive/folders/13oVPMyoBgNwEAsE_Ad3XVI1W5cNqfvrq). Then, you may unzip a dataset and put it in [datasets](datasets/).
To try the examples, you may download `hmdb6` dataset, which is a subset of `hmdb51` with only 6 classes. All the datasets can be downloaded from [Google Drive](https://drive.google.com/drive/folders/13oVPMyoBgNwEAsE_Ad3XVI1W5cNqfvrq). Then, you may unzip a dataset and put it in [datasets](datasets/). You may also try STGCN for skeleton-based action recogonition on `kinetics36`, which is a subset of Kinetics dataset with 36 classes.
### Fitting and saving a pipeline
```
python3 examples/fit.py
```
Some important hyperparameters are as follows.
* `--alg`: the supported algorithm. Currently we support `tsn`, `tsm`, `i3d`, `eco`, `eco_full`, `c3d`, `r2p1d`, and `r3d`.
* `--alg`: the supported algorithm. Currently we support `tsn`, `tsm`, `i3d`, `eco`, `eco_full`, `c3d`, `r2p1d`, `r3d`, `stgcn`.
* `--pretrained`: whether loading pre-trained weights and fine-tuning.
* `--gpu`: which gpu device to use. Empty string for CPU.
* `--data_dir`: the directory of the dataset
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## Supported Algorithms

| Algorithms | Primitive Path | Paper |
| :--------: | :----------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------- |
| TSN | [autovideo/recognition/tsn_primitive.py](autovideo/recognition/tsn_primitive.py) | [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://arxiv.org/abs/1608.00859) |
| TSM | [autovideo/recognition/tsm_primitive.py](autovideo/recognition/tsm_primitive.py) | [TSM: Temporal Shift Module for Efficient Video Understanding](https://arxiv.org/abs/1811.08383) |
| :--------: | :----------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------- |
| TSN | [autovideo/recognition/tsn_primitive.py](autovideo/recognition/tsn_primitive.py) | [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://arxiv.org/abs/1608.00859) |
| TSM | [autovideo/recognition/tsm_primitive.py](autovideo/recognition/tsm_primitive.py) | [TSM: Temporal Shift Module for Efficient Video Understanding](https://arxiv.org/abs/1811.08383) |
| R2P1D | [autovideo/recognition/r2p1d_primitive.py](autovideo/recognition/r2p1d_primitive.py) | [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/abs/1711.11248) |
| R3D | [autovideo/recognition/r3d_primitive.py](autovideo/recognition/r3d_primitive.py) | [Learning spatio-temporal features with 3d residual networks for action recognition](https://arxiv.org/abs/1708.07632) |
| C3D | [autovideo/recognition/c3d_primitive.py](autovideo/recognition/c3d_primitive.py) | [Learning Spatiotemporal Features with 3D Convolutional Networks](https://arxiv.org/abs/1412.0767) |
| ECO-Lite | [autovideo/recognition/eco_primitive.py](autovideo/recognition/eco_primitive.py) | [ECO: Efficient Convolutional Network for Online Video Understanding](https://arxiv.org/abs/1804.09066) |
| ECO-Full | [autovideo/recognition/eco_full_primitive.py](autovideo/recognition/eco_full_primitive.py) | [ECO: Efficient Convolutional Network for Online Video Understanding](https://arxiv.org/abs/1804.09066) |
| I3D | [autovideo/recognition/i3d_primitive.py](autovideo/recognition/i3d_primitive.py) | [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://arxiv.org/abs/1705.07750) |
| STGCN (under testing, coming soon) | [autovideo/recognition/stgcn_primitive.py](https://github.com/datamllab/autovideo/blob/Stgcn_Jiaben/autovideo/recognition/stgcn_primitive.py) | [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://arxiv.org/abs/1801.07455) |
| R3D | [autovideo/recognition/r3d_primitive.py](autovideo/recognition/r3d_primitive.py) | [Learning spatio-temporal features with 3d residual networks for action recognition](https://arxiv.org/abs/1708.07632) |
| C3D | [autovideo/recognition/c3d_primitive.py](autovideo/recognition/c3d_primitive.py) | [Learning Spatiotemporal Features with 3D Convolutional Networks](https://arxiv.org/abs/1412.0767) |
| ECO-Lite | [autovideo/recognition/eco_primitive.py](autovideo/recognition/eco_primitive.py) | [ECO: Efficient Convolutional Network for Online Video Understanding](https://arxiv.org/abs/1804.09066) |
| ECO-Full | [autovideo/recognition/eco_full_primitive.py](autovideo/recognition/eco_full_primitive.py) | [ECO: Efficient Convolutional Network for Online Video Understanding](https://arxiv.org/abs/1804.09066) |
| I3D | [autovideo/recognition/i3d_primitive.py](autovideo/recognition/i3d_primitive.py) | [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://arxiv.org/abs/1705.07750) |
| STGCN | [autovideo/recognition/stgcn_primitive.py](autovideo/recognition/stgcn_primitive.py) | [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://arxiv.org/abs/1801.07455) |

## Advanced Usage
Beyond the above examples, you can also customize the configurations.
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