Skip to content

Latest commit

 

History

History
71 lines (54 loc) · 3.42 KB

README.md

File metadata and controls

71 lines (54 loc) · 3.42 KB
Raw

C3D

Introduction

@ARTICLE{2014arXiv1412.0767T,
author = {Tran, Du and Bourdev, Lubomir and Fergus, Rob and Torresani, Lorenzo and Paluri, Manohar},
title = "{Learning Spatiotemporal Features with 3D Convolutional Networks}",
keywords = {Computer Science - Computer Vision and Pattern Recognition},
year = 2014,
month = dec,
eid = {arXiv:1412.0767}
}

@article{Tran2014C3DGF,
  title={C3D: Generic Features for Video Analysis},
  author={D. Tran and Lubomir D. Bourdev and R. Fergus and L. Torresani and Manohar Paluri},
  journal={ArXiv},
  year={2014},
  volume={abs/1412.0767}
}

Model Zoo

UCF-101

config resolution gpus backbone pretrain top1 acc top5 acc testing protocol inference_time(video/s) gpu_mem(M) ckpt log json
c3d_sports1m_16x1x1_45e_ucf101_rgb.py 128x171 8 c3d sports1m 83.27 95.90 10 clips x 1 crop x 6053 ckpt log json

Notes:

  1. The author of C3D normalized UCF-101 with volume mean and used SVM to classify videos, while we normalized the dataset with RGB mean value and used a linear classifier.
  2. 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 * 2 video/gpu and lr=0.08 for 16 GPUs * 4 video/gpu.
  3. 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 UCF-101 in Data Preparation.

Train

You can use the following command to train a model.

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

Example: train C3D model on UCF-101 dataset in a deterministic option with periodic validation.

python tools/train.py configs/recognition/c3d/c3d_sports1m_16x1x1_45e_ucf101_rgb.py \
    --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 C3D model on UCF-101 dataset and dump the result to a json file.

python tools/test.py configs/recognition/c3d/c3d_sports1m_16x1x1_45e_ucf101_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy

For more details, you can refer to Test a dataset part in getting_started.