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@inproceedings{inproceedings,
author = {Carreira, J. and Zisserman, Andrew},
year = {2017},
month = {07},
pages = {4724-4733},
title = {Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset},
doi = {10.1109/CVPR.2017.502}
}
@article{NonLocal2018,
author = {Xiaolong Wang and Ross Girshick and Abhinav Gupta and Kaiming He},
title = {Non-local Neural Networks},
journal = {CVPR},
year = {2018}
}
config | resolution | gpus | backbone | pretrain | top1 acc | top5 acc | inference_time(video/s) | gpu_mem(M) | ckpt | log | json |
---|---|---|---|---|---|---|---|---|---|---|---|
i3d_r50_32x2x1_100e_kinetics400_rgb | 340x256 | 8 | ResNet50 | ImageNet | 72.68 | 90.78 | 1.7 (320x3 frames) | 5170 | ckpt | log | json |
i3d_r50_32x2x1_100e_kinetics400_rgb | short-side 256 | 8 | ResNet50 | ImageNet | 73.27 | 90.92 | x | 5170 | ckpt | log | json |
i3d_r50_video_32x2x1_100e_kinetics400_rgb | short-side 256p | 8 | ResNet50 | ImageNet | 72.85 | 90.75 | x | 5170 | ckpt | log | json |
i3d_r50_dense_32x2x1_100e_kinetics400_rgb | 340x256 | 8x2 | ResNet50 | ImageNet | 72.77 | 90.57 | 1.7 (320x3 frames) | 5170 | ckpt | log | json |
i3d_r50_dense_32x2x1_100e_kinetics400_rgb | short-side 256 | 8 | ResNet50 | ImageNet | 73.48 | 91.00 | x | 5170 | ckpt | log | json |
i3d_r50_lazy_32x2x1_100e_kinetics400_rgb | 340x256 | 8 | ResNet50 | ImageNet | 72.32 | 90.72 | 1.8 (320x3 frames) | 5170 | ckpt | log | json |
i3d_r50_lazy_32x2x1_100e_kinetics400_rgb | short-side 256 | 8 | ResNet50 | ImageNet | 73.24 | 90.99 | x | 5170 | ckpt | log | json |
i3d_nl_embedded_gaussian_r50_32x2x1_100e_kinetics400_rgb | short-side 256p | 8x4 | ResNet50 | ImageNet | 74.71 | 91.81 | x | 6438 | ckpt | log | json |
i3d_nl_gaussian_r50_32x2x1_100e_kinetics400_rgb | short-side 256p | 8x4 | ResNet50 | ImageNet | 73.37 | 91.26 | x | 4944 | ckpt | log | json |
i3d_nl_dot_product_r50_32x2x1_100e_kinetics400_rgb | short-side 256p | 8x4 | ResNet50 | ImageNet | 73.92 | 91.59 | x | 4832 | ckpt | log | json |
Notes:
For more details on data preparation, you can refer to Kinetics400 in Data Preparation.
You can use the following command to train a model.
python tools/train.py ${CONFIG_FILE} [optional arguments]
Example: train I3D model on Kinetics-400 dataset in a deterministic option with periodic validation.
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.
You can use the following command to test a model.
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.
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 --average-clips prob
For more details, you can refer to Test a dataset part in getting_started.
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