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CSN

Introduction

@inproceedings{inproceedings,
author = {Wang, Heng and Feiszli, Matt and Torresani, Lorenzo},
year = {2019},
month = {10},
pages = {5551-5560},
title = {Video Classification With Channel-Separated Convolutional Networks},
doi = {10.1109/ICCV.2019.00565}
}
@inproceedings{ghadiyaram2019large,
  title={Large-scale weakly-supervised pre-training for video action recognition},
  author={Ghadiyaram, Deepti and Tran, Du and Mahajan, Dhruv},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={12046--12055},
  year={2019}
}

Model Zoo

Kinetics-400

config resolution gpus backbone pretrain top1 acc top5 acc inference_time(video/s) gpu_mem(M) ckpt log json
ircsn_ig65m_pretrained_bnfrozen_r50_32x2x1_58e_kinetics400_rgb short-side 320 x ResNet50 IG65M 79.0 94.2 x x infer_ckpt x x
ircsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb short-side 320 x ResNet152 None 76.5 92.1 x x infer_ckpt x x
ircsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb short-side 320 x ResNet152 Sports1M 78.2 93.0 x x infer_ckpt x x
ircsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb.py short-side 320 8x4 ResNet152 IG65M 82.76/82.6 95.68/95.3 x 8516 ckpt/infer_ckpt log json
ipcsn_bnfrozen_r152_32x2x1_180e_kinetics400_rgb short-side 320 x ResNet152 None 77.8 92.8 x x infer_ckpt x x
ipcsn_sports1m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb short-side 320 x ResNet152 Sports1M 78.8 93.5 x x infer_ckpt x x
ipcsn_ig65m_pretrained_bnfrozen_r152_32x2x1_58e_kinetics400_rgb short-side 320 x ResNet152 IG65M 82.5 95.3 x x infer_ckpt x x
ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb.py short-side 320 8x4 ResNet152 IG65M 80.14 94.93 x 8517 ckpt log json

Notes:

  1. The gpus indicates the number of gpu (32G V100) 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.
  3. The validation set of Kinetics400 we used consists of 19796 videos. These videos are available at Kinetics400-Validation. The corresponding data list (each line is of the format 'video_id, num_frames, label_index') and the label map are also available.
  4. The infer_ckpt means those checkpoints are ported from VMZ.

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 CSN model on Kinetics-400 dataset in a deterministic option with periodic validation.

python tools/train.py configs/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_kinetics400_rgb.py \
    --work-dir work_dirs/ircsn_ig65m_pretrained_r152_32x2x1_58e_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 CSN model on Kinetics-400 dataset and dump the result to a json file.

python tools/test.py configs/recognition/csn/ircsn_ig65m_pretrained_r152_32x2x1_58e_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.