README.md

# I3D

## Model Zoo

### Kinetics-400

|config | backbone |pretrain| top1 acc| top5 acc | gpu_mem(M)| ckpt | log|
|-|-|-|-|-|-|-|-|
|[i3d_r34_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r34_32x2x1_100e_kinetics400_rgb.py) | ResNet34|ImageNet |68.37|88.15|3176| [ckpt]() | [log]()|
|[i3d_r50_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py) | ResNet50|ImageNet |72.68|90.78|5170|[ckpt]() | [log]()|
|[i3d_r50_dense_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_dense_32x2x1_100e_kinetics400_rgb.py) | ResNet50| ImageNet|72.77|90.57|5170| [ckpt]() | [log]()|
|[i3d_r50_fast_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_fast_32x2x1_100e_kinetics400_rgb.py) | ResNet50 |ImageNet|72.32|90.72|5170| [ckpt]() | [log]()|
|[i3d_r50_video_3d_32x2x1_100e_kinetics400_rgb](/configs/recognition/i3d/i3d_r50_video_32x2x1_100e_kinetics400_rgb.py) | ResNet50| ImageNet| x | x | x| [ckpt]() | [log]()|

For more details on data preparation, you can refer to Kinetics400 in [Data Preparation](/docs/data_preparation.md).

## Train
You can use the following command to train a model.
```shell
python tools/train.py ${CONFIG_FILE} [optional arguments]
```

Example: train I3D model on Kinetics-400 dataset in a deterministic option with periodic validation.
```shell
python tools/train.py configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py \
    --work_dir work_dirs/i3d_r50_32x2x1_100e_kinetics400_rgb \
    --validate --seed 0 --deterministic
```

For more details, you can refer to **Training setting** part in [getting_started](/docs/getting_started.md).

## Test
You can use the following command to test a model.
```shell
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
```

Example: test I3D model on Kinetics-400 dataset and dump the result to a json file.
```shell
python tools/test.py configs/recognition/i3d/i3d_r50_32x2x1_100e_kinetics400_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
    --out result.json
```

For more details, you can refer to **Test a dataset** part in [getting_started](/docs/getting_started.md).