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R2Plus1D-mxnet

R2Plus1D MXNet Implementation

R2Plus1D: A Closer Look at Spatiotemporal Convolutions for Action Recognition (CVPR 2018)

Caffe2 Implementation: https://github.com/facebookresearch/R2Plus1D

Dataset

Result

Achieved 92.6% Accuracy(Clip@1, prediction using only 1 clip) on UCF101 Dataset, which is 1.3% higher than the original Caffe2 model(Accuracy 91.3%).

Usage

Requirements

MXNet with GPU support
opencv

Data Preparation

Download and extract UCF101 dataset to ~/UCF101
Download pre-trained model from Caffe2 Pre-trained model to ~/r2.5d_d34_l32.pkl

Training

$  python train.py --gpus 0,1,2,3,4,5,6,7 --pretrained ~/r2.5d_d34_l32.pkl --output ~/r2plus1d_output --batch_per_device 4 --lr 1e-4 
--model_depth 34 --wd 0.005 --num_class 101 --num_epoch 80

Testing

Assume the training output directory is ~/r2plus1d_output and the epoch number we want to test is 80.

$ python validation.py --gpus 0 --output ~/r2plus1d_output --eval_epoch 80 --batch_per_device 48 --model_prefix test