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ACRN

Introduction

@inproceedings{gu2018ava,
  title={Ava: A video dataset of spatio-temporally localized atomic visual actions},
  author={Gu, Chunhui and Sun, Chen and Ross, David A and Vondrick, Carl and Pantofaru, Caroline and Li, Yeqing and Vijayanarasimhan, Sudheendra and Toderici, George and Ricco, Susanna and Sukthankar, Rahul and others},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6047--6056},
  year={2018}
}
@inproceedings{sun2018actor,
  title={Actor-centric relation network},
  author={Sun, Chen and Shrivastava, Abhinav and Vondrick, Carl and Murphy, Kevin and Sukthankar, Rahul and Schmid, Cordelia},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={318--334},
  year={2018}
}

Model Zoo

AVA2.1

Model Modality Pretrained Backbone Input gpus mAP log json ckpt
slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava_rgb RGB Kinetics-400 ResNet50 32x2 8 27.1 log json ckpt

AVA2.2

Model Modality Pretrained Backbone Input gpus mAP log json ckpt
slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb RGB Kinetics-400 ResNet50 32x2 8 27.8 log json ckpt
  • Notes:
  1. The gpus indicates the number of gpu we used to get the checkpoint. 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.

For more details on data preparation, you can refer to AVA in Data Preparation.

Train

You can use the following command to train a model.

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

Example: train ACRN with SlowFast backbone on AVA with periodic validation.

python tools/train.py configs/detection/acrn/slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py --validate

For more details and optional arguments infos, 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 ACRN with SlowFast backbone on AVA and dump the result to a csv file.

python tools/test.py configs/detection/acrn/slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py checkpoints/SOME_CHECKPOINT.pth --eval mAP --out results.csv

For more details and optional arguments infos, you can refer to Test a dataset part in getting_started .