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R2plus1D

Introduction

[ALGORITHM]

@inproceedings{tran2018closer,
  title={A closer look at spatiotemporal convolutions for action recognition},
  author={Tran, Du and Wang, Heng and Torresani, Lorenzo and Ray, Jamie and LeCun, Yann and Paluri, Manohar},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={6450--6459},
  year={2018}
}

Model Zoo

Kinetics-400

config resolution gpus backbone pretrain top1 acc top5 acc inference_time(video/s) gpu_mem(M) ckpt log json
r2plus1d_r34_8x8x1_180e_kinetics400_rgb short-side 256 8x4 ResNet34 None 67.30 87.65 x 5019 ckpt log json
r2plus1d_r34_video_8x8x1_180e_kinetics400_rgb short-side 256 8 ResNet34 None 67.3 87.8 x 5019 ckpt log json
r2plus1d_r34_8x8x1_180e_kinetics400_rgb short-side 320 8x2 ResNet34 None 68.68 88.36 1.6 (80x3 frames) 5019 ckpt log json
r2plus1d_r34_32x2x1_180e_kinetics400_rgb short-side 320 8x2 ResNet34 None 74.60 91.59 0.5 (320x3 frames) 12975 ckpt log json

Notes:

  1. 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.
  2. 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.

Train

You can use the following command to train a model.

python tools/train.py ${CONFIG_FILE} [optional arguments]

Example: train R(2+1)D model on Kinetics-400 dataset in a deterministic option with periodic validation.

python tools/train.py configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_180e_kinetics400_rgb.py \
    --work-dir work_dirs/r2plus1d_r34_3d_8x8x1_180e_kinetics400_rgb \
    --validate --seed 0 --deterministic

For more details, you can refer to Training setting part in getting_started.

Test

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Example: test R(2+1)D model on Kinetics-400 dataset and dump the result to a json file.

python tools/test.py configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_180e_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.