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TIN

Introduction

[ALGORITHM]

@article{shao2020temporal,
    title={Temporal Interlacing Network},
    author={Hao Shao and Shengju Qian and Yu Liu},
    year={2020},
    journal={AAAI},
}

Model Zoo

Something-Something V1

config resolution gpus backbone pretrain top1 acc top5 acc reference top1 acc reference top5 acc gpu_mem(M) ckpt log json
tin_r50_1x1x8_40e_sthv1_rgb height 100 8x4 ResNet50 ImageNet 44.25 73.94 44.04 72.72 6181 ckpt log json

Something-Something V2

config resolution gpus backbone pretrain top1 acc top5 acc reference top1 acc reference top5 acc gpu_mem(M) ckpt log json
tin_r50_1x1x8_40e_sthv2_rgb height 240 8x4 ResNet50 ImageNet 56.70 83.62 56.48 83.45 6185 ckpt log json

Kinetics-400

config resolution gpus backbone pretrain top1 acc top5 acc gpu_mem(M) ckpt log json
tin_tsm_finetune_r50_1x1x8_50e_kinetics400_rgb short-side 256 8x4 ResNet50 TSM-Kinetics400 70.89 89.89 6187 ckpt log json

Here, we use finetune to indicate that we use TSM model trained on Kinetics-400 to finetune the TIN model on Kinetics-400.

Notes:

  1. The reference topk acc are got by training the original repo #1aacd0c with no AverageMeter issue. The AverageMeter issue will lead to incorrect performance, so we fix it before running.
  2. 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.
  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.
  4. The values in columns named after "reference" are the results got by training on the original repo, using the same model settings.

For more details on data preparation, you can refer to Kinetics400, Something-Something V1 and Something-Something V2 in Data Preparation.

Train

You can use the following command to train a model.

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

Example: train TIN model on Something-Something V1 dataset in a deterministic option with periodic validation.

python tools/train.py configs/recognition/tin/tin_r50_1x1x8_40e_sthv1_rgb.py \
    --work-dir work_dirs/tin_r50_1x1x8_40e_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 TIN model on Something-Something V1 dataset and dump the result to a json file.

python tools/test.py configs/recognition/tin/tin_r50_1x1x8_40e_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.