Skip to content

Latest commit

 

History

History
78 lines (54 loc) · 4.23 KB

README.md

File metadata and controls

78 lines (54 loc) · 4.23 KB
Raw

TRN

Introduction

@article{zhou2017temporalrelation,
    title = {Temporal Relational Reasoning in Videos},
    author = {Zhou, Bolei and Andonian, Alex and Oliva, Aude and Torralba, Antonio},
    journal={European Conference on Computer Vision},
    year={2018}
}

Model Zoo

Something-Something V1

config resolution gpus backbone pretrain top1 acc (efficient/accurate) top5 acc (efficient/accurate) gpu_mem(M) ckpt log json
trn_r50_1x1x8_50e_sthv1_rgb height 100 8 ResNet50 ImageNet 31.62 / 33.88 60.01 / 62.12 11010 ckpt log json

Something-Something V2

config resolution gpus backbone pretrain top1 acc (efficient/accurate) top5 acc (efficient/accurate) gpu_mem(M) ckpt log json
trn_r50_1x1x8_50e_sthv2_rgb height 100 8 ResNet50 ImageNet 45.14 / 47.96 73.21 / 75.97 11010 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. There are two kinds of test settings for Something-Something dataset, efficient setting (center crop x 1 clip) and accurate setting (Three crop x 2 clip).
  3. In the original repository, the author augments data with random flipping on something-something dataset, but the augmentation method may be wrong due to the direct actions, such as push left to right. So, we replaced flip with flip with label mapping, and change the testing method TenCrop, which has five flipped crops, to Twice Sample & ThreeCrop.
  4. We use ResNet50 instead of BNInception as the backbone of TRN. When Training TRN-ResNet50 on sthv1 dataset in the original repository, we get top1 (top5) accuracy 30.542 (58.627) vs. ours 31.62 (60.01).

For more details on data preparation, you can refer to

Train

You can use the following command to train a model.

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

Example: train TRN model on sthv1 dataset in a deterministic option with periodic validation.

python tools/train.py configs/recognition/trn/trn_r50_1x1x8_50e_sthv1_rgb.py \
    --work-dir work_dirs/trn_r50_1x1x8_50e_sthv1_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 TRN model on sthv1 dataset and dump the result to a json file.

python tools/test.py configs/recognition/trn/trn_r50_1x1x8_50e_sthv1_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
    --out result.json

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