3D_Network

3D Network for Video Recognition

Requirements

Please follow the installation instructions in INSTALL.md.

Implementation Details

1. We uniformly sample 32 frames during training and inference.
2. We use 1-clip 1-crop evaluation for 3D network with the resolution of 256x256 following the original implementation.
3. TRAIN.LAMBDA_AV denotes the coefficient $\lambda$ in the loss function and we set it to be 0.75.
4. We train 3D network SlowFast with 4 NVIDIA Tesla V100 (32GB) cards and the model is pretrained on Kinetics400 before training on Something-Something V1.

Training

1. Specify the directory of datasets with DATA.PATH_PREFIX in exp/slowfast_sthv1/run.sh.

2. Specify the directory of output with OUTPUT_DIR in run.sh.

3. Download the pretrained model on Kinetics400 from the original repo and specify the path with CHECKPOINT_FILE_PATH in configs/sth/SLOWFAST_8x8_R50_FFN.yaml.

4. Simply run the training scripts in exp as followed:

   bash exp/slowfast_sthv1/run.sh  ## baseline training
   bash exp/slowfast_sthv1/run_MCA.sh   ## MCA training
   

Inference

1. Specify the directory of datasets with DATA.PATH_PREFIX in exp/slowfast_sthv1/test.sh.

2. Please download pretrained models from Google Drive.

3. Specify the directory of the pretrained model with TEST.CHECKPOINT_FILE_PATH in test.sh.

4. Run the inference scripts in exp as followed:

   bash exp/slowfast_sthv1/test.sh