[ALGORITHM]
@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}
}
[BACKBONE]
@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:
- The gpus indicates the number of gpu we used to get the checkpoint. It is noteworthy that the configs we provide are used for 8 gpus as default. According to the Linear Scaling Rule, you may set the learning rate proportional to the batch size if you use different GPUs or videos per GPU, e.g., lr=0.01 for 4 GPUs x 2 video/gpu and lr=0.08 for 16 GPUs x 4 video/gpu.
- The inference_time is got by this benchmark script, where we use the sampling frames strategy of the test setting and only care about the model inference time, not including the IO time and pre-processing time. For each setting, we use 1 gpu and set batch size (videos per gpu) to 1 to calculate the inference time.
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.