vra / Action Recognition Using 3d Resnet
Use 3D ResNet to extract features of UCF101 and HMDB51 and then classify them.
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action-recognition-using-3d-resnet
Use 3D ResNet to extract features of UCF101 and HMDB51 and then classify them.
how to use
- Clone this repo:
git clone https://github.com/vra/action-recognition-using-3d-resnet.git
-
Download 3D ResNet
-
Download its pretrained models, put these models to this repo's
data/models/ -
run the script under
scriptsunder to extract 3D resnet features of UCF101 and HMDB51:
bash scripts/extract_resnet_3d_features.sh /path/to/video-classification/3d-cnn-pytorch ucf101 /path/to/ucf101/videos
bash scripts/extract_resnet_3d_features.sh /path/to/video-classification/3d-cnn-pytorch hmdb51 /path/to/hmdb51/videos
Also, you can download my extracted features of ucf101 and hmdb51 at here and here. Remember to put the first one to data/jsons/ucf101 before you download the second one, otherwise the first one will be convered.
- Run
main.pyto classify extracted 3D resnet features:
python main.py -dataset hmdb51
Results:
| strategy | dataset | accuracy |
|---|---|---|
| mean | ucf101 | 0.8487 |
| max | ucf101 | 0.8667 |
| mean | hmdb51 | 0.5425 |
| max | hmdb51 | 0.5399 |
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