TSN-surveillance
Now in experimental release, suggestions welcome.
Note: always use git clone --recursive https://github.com/yjxiong/tsn-pytorch
to clone original tsn-pytorch.
Dataset : Trimmed UCF Crimes - https://github.com/henniekim/action_recognition_study/wiki/Temporal-annotation-for-UCF-Crimes-dataset
Note: How I set for each model is in table.
For RGB models:
seg5 | seg10 | seg15 | seg20 | |
---|---|---|---|---|
dropout 0.4 | x | o | o | o |
dropout 0.6 | o | o | o | o |
dropout 0.8 | x | o | o | o |
/datahdd/workdir/hyeongeun/anaconda3/envs/test2/bin/python3.6 /datahdd/workdir/hyeongeun/test/tsn-pytorch/main.py ucf101 RGB /datahdd/Dataset/Trimmed_UCF_Crimes/trim_shooting_re_trainingset.txt /datahdd/Dataset/Trimmed_UCF_Crimes/trim_shooting_re_testset.txt --arch BNInception --num_segments 10 --gd 20 --lr 0.0001 --lr_steps 30 60 --epochs 300 -b 32 -j 2 --dropout 0.6 --snapshot_pref re_RGB_seg10_drop06
For flow models:
seg5 | seg10 | seg15 | seg20 | |
---|---|---|---|---|
dropout 0.6 | o | o | o | o |
/datahdd/workdir/hyeongeun/anaconda3/envs/test2/bin/python3.6 /datahdd/workdir/hyeongeun/test/tsn-pytorch/main.py ucf101 Flow /datahdd/Dataset/Trimmed_UCF_Crimes/trim_shooting_re_trainingset.txt /datahdd/Dataset/Trimmed_UCF_Crimes/trim_shooting_re_testset.txt --arch BNInception --num_segments 10 --gd 20 --lr 0.0001 --lr_steps 30 60 --epochs 300 -b 32 -j 2 --dropout 0.6 --snapshot_pref re_Flow_seg10_drop06
For Warp models:
seg10 | seg15 | seg20 | |
---|---|---|---|
dropout 0.6 | o | o | o |
/datahdd/workdir/hyeongeun/anaconda3/envs/test2/bin/python3.6 /datahdd/workdir/hyeongeun/test/tsn-pytorch/main.py ucf101 Flow /datahdd/Dataset/Trimmed_UCF_Crimes/trim_shooting_re_warp_trainingset.txt /datahdd/Dataset/Trimmed_UCF_Crimes/trim_shooting_re_warp_testset.txt --arch BNInception --num_segments 10 --gd 20 --lr 0.0001 --lr_steps 30 60 --epochs 300 -b 16 -j 2 --dropout 0.6 --snapshot_pref re_Warp_seg10_drop06
After training, there will checkpoints saved by pytorch, for example re_RGB_seg10_drop06_checkpoint.pth
. Also, there will best point saved, for example re_RGB_seg10_drop06_best.pth
Use the following command to test its performance in the standard TSN testing protocol:
for RGB models:
/datahdd/workdir/hyeongeun/anaconda3/envs/test2/bin/python3.6 /datahdd/workdir/hyeongeun/test/tsn-pytorch/test_models.py ucf101 RGB /datahdd/Dataset/Trimmed_UCF_Crimes/trim_shooting_re_testset.txt /datahdd/Dataset/Trimmed_UCF_Crimes/TSN_result/re_RGB_seg10_epoch300/re_RGB_seg10_drop06_best.pth --test_segments 10 --arch BNInception --save_scores re_RGB_seg10_dp06
for Flow models:
/datahdd/workdir/hyeongeun/anaconda3/envs/test2/bin/python3.6 /datahdd/workdir/hyeongeun/test/tsn-pytorch/test_models.py ucf101 Flow /datahdd/Dataset/Trimmed_UCF_Crimes/trim_shooting_re_testset.txt /datahdd/Dataset/Trimmed_UCF_Crimes/TSN_result/re_Flow_adam_seg10/re_flow_seg10_best.pth --test_segments 10 --arch BNInception --save_scores re_Flow_seg10_dp06
for Warp models:
/datahdd/workdir/hyeongeun/anaconda3/envs/test2/bin/python3.6 /datahdd/workdir/hyeongeun/test/tsn-pytorch/test_models.py ucf101 Flow /datahdd/Dataset/Trimmed_UCF_Crimes/trim_shooting_re_warp_testset.txt /datahdd/Dataset/Trimmed_UCF_Crimes/TSN_result/re_Warp_seg10/re_Warp_seg10_drop06_best.pth --test_segments 10 --dropout 0.6 --arch BNInception --save_scores re_Warp_seg10_dp06
for RGB models:
seg5 | seg10 | seg15 | seg20 | ||
---|---|---|---|---|---|
dropout 0.4 | top1 | 41.71% | 36.57% | 40% | |
. | top3 | 59.43% | 57.14% | 61.14% | |
dropout 0.6 | top1 | 37.71% | 43.43% | 41.14% | 40% |
. | top3 | 55.43% | 59.43% | 62.86% | 60.57% |
dropout 0.8 | top1 | 40.57% | 38.29% | 36.57% | |
. | top3 | 61.71% | 59.43% | 57.71% |
for Flow models:
seg5 | seg10 | seg15 | seg20 | ||
---|---|---|---|---|---|
dropout 0.6 | top1 | 42.29% | 39.43% | 35.43% | 38.86% |
. | top3 | 63.43% | 60.57% | 59.43% | 61.14% |
for Warp models:
seg10 | seg15 | seg20 | ||
---|---|---|---|---|
dropout 0.6 | top1 | 26.29% | 30.29% | 30.29% |
. | top3 | 54.86% | 55.43% | 53.14% |
# This file may be used to create an environment using:# $ conda create --name <env> --file <this file># platform: linux-64blas=1.0=mkl ca-certificates=2018.03.07=0 certifi=2018.4.16=py36_0 cffi=1.11.5=py36h9745a5d_0 cuda80=1.0=0 cudatoolkit=8.0=3 cudnn=7.0.5=cuda8.0_0 freetype=2.8=hab7d2ae_1 intel-openmp=2018.0.0=8 jpeg=9b=h024ee3a_2 libedit=3.1.20170329=h6b74fdf_2 libffi=3.2.1=hd88cf55_4 libgcc-ng=7.2.0=hdf63c60_3 libgfortran-ng=7.2.0=hdf63c60_3 libpng=1.6.34=hb9fc6fc_0 libstdcxx-ng=7.2.0=hdf63c60_3 libtiff=4.0.9=he85c1e1_1 mkl=2018.0.2=1 mkl_fft=1.0.1=py36h3010b51_0 mkl_random=1.0.1=py36h629b387_0 ncurses=6.1=hf484d3e_0 numpy=1.14.3=py36hcd700cb_1 numpy-base=1.14.3=py36h9be14a7_1 olefile=0.45.1=py36_0 openssl=1.0.2o=h20670df_0 pillow=5.1.0=py36h3deb7b8_0 pip=10.0.1=py36_0 pycparser=2.18=py36hf9f622e_1 python=3.6.5=hc3d631a_2 pytorch=0.3.1=py36_cuda8.0.61_cudnn7.0.5_2 pyyaml=3.12=py36hafb9ca4_1 readline=7.0=ha6073c6_4 scikit-learn=0.19.1=py36h7aa7ec6_0 scipy=1.1.0=py36hfc37229_0 setuptools=39.1.0=py36_0 six=1.11.0=py36h372c433_1 sqlite=3.23.1=he433501_0 tk=8.6.7=hc745277_3 torchvision=0.2.0=py36_0 wheel=0.31.1=py36_0 xz=5.2.4=h14c3975_4 yaml=0.1.7=had09818_2 zlib=1.2.11=ha838bed_2
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