i3d

I3D

Introduction

[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}
}

Model Zoo

Kinetics-400

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:

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 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.

Test

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.