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TSM

Model Zoo

Kinetics-400

config resolution gpus backbone pretrain top1 acc top5 acc reference top1 acc reference top5 acc inference_time(video/s) gpu_mem(M) ckpt log json
tsm_r50_1x1x8_50e_kinetics400_rgb 340x256 8 ResNet50 ImageNet 70.24 89.56 70.36 89.49 74.0 (8x1 frames) 7079 ckpt log json
tsm_r50_dense_1x1x8_100e_kinetics400_rgb 340x256 8x4 ResNet50 ImageNet 72.9 90.44 72.22 90.37 11.5 (8x10 frames) 7079 ckpt log json
tsm_r50_1x1x16_50e_kinetics400_rgb 340x256 8 ResNet50 ImageNet 71.69 90.4 70.67 89.98 47.0 (16x1 frames) 10404 ckpt log json

Something-Something V1

config resolution gpus backbone pretrain top1 acc top5 acc reference top1 acc reference top5 acc gpu_mem(M) ckpt log json
tsm_r50_1x1x8_50e_sthv1_rgb short-side 100 8 ResNet50 ImageNet 44.62 75.51 42.08 72.66 7077 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
tsm_r50_1x1x16_50e_sthv2_rgb short-side 240 8 ResNet50 ImageNet 57.68 83.65 56.57 84.30 10400 ckpt log json
tsm_r101_1x1x8_50e_sthv2_rgb short-side 240 8 ResNet101 ImageNet 59.12 85.74 59.20 85.27 9784 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 * 2 video/gpu and lr=0.08 for 16 GPUs * 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 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 TSM model on Kinetics-400 dataset in a deterministic option with periodic validation.

python tools/train.py configs/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_rgb.py \
    --work-dir work_dirs/tsm_r50_1x1x8_100e_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 TSM model on Kinetics-400 dataset and dump the result to a json file.

python tools/test.py configs/recognition/tsm/tsm_r50_1x1x8_50e_kinetics400_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.