# Multi-Scale Receptive Fields Convolutional Network for Action Recognition
## 4 Experiments
### Experimental Settings and Datasets
#### 4.1.2 Training & Testing  
At the training stage, we follow [39] to divide each video into four segments, each picking up only one video frame randomly. The training and testing is performed for this paper on two cards of Nvidia GTX 1080 Ti with 11G memory. Data augmentation is then applied to these selected frames. First, every frame is rescaled to make the smaller side no more than 256. Second, a \(224\times 224\) patch is randomly cropped from each rescaled frame. Last, these cropped frames are randomly horizontally flipped and RGB jittered  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **Layer** & **Output** \\ \hline raw clip & - & \(4\times 224\times 224\) \\ \hline conv1 & \(1\times 7\times 7\), 64 & \(4\times 112\times 112\) \\ \hline pool1 & \(1\times 3\times 3\) Max & \(4\times 56\times 56\) \\ \hline res2 & \(\begin{bmatrix}1\times 1\times 1,64\\ 1\times 3\times 3\;(dr^{1}=1),64\\ \hline 1\times 3\times 3\;(dr=2),64\\ \hline 1\times 3\times 3\;(dr=3),64\\ 1\times 1\times 1,256\end{bmatrix}\times 3\) & \(4\times 56\times 56\) \\ \hline res3 & \(\begin{bmatrix}1\times 1\times 1,128\\ 1\times 3\times 3\;(dr=1),128\\ \hline 1\times 3\times 3\;(dr=2),128\\ \hline 1\times 1\times 1,512\end{bmatrix}\times 4\) & \(4\times 28\times 28\) \\ \hline res4 & \(\begin{bmatrix}3\times 1\times 1,256\\ 1\times 3\times 3\;(dr=1),256\\ \hline 1\times 3\times 3\;(dr=2),256\\ \hline 1\times 3\times 3\;(dr=3),256\\ 1\times 1\times 1,1024\end{bmatrix}\times 6\) & \(4\times 14\times 14\) \\ \hline res5 & \(\begin{bmatrix}3\times 1\times 1,512\\ 1\times 3\times 3\;(dr=1),512\\ \hline 1\times 3\times 3\;(dr=2),512\\ 1\times 3\times 3\;(dr=3),512\\ 1\times 1\times 1,2048\end{bmatrix}\times 3\) & \(4\times 7\times 7\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Architectures for MSRFNet. (“dr”means dilation rate).  
after the cropping step. MSRFNet is trained with the method of mini-batch stochastic gradient descent with momentum. The momentum is set to 0.9 and the weight decay is set to 0.0001. The batch size is 24. We do not use a larger batch size due to the limitation of GPU devices. The learning rate is initialized as 0.01, which is reduced at two training checkpoints. For HMDB51 and UCF101, the whole training process contains 40 epochs. At epoch 20 and 30, the leaning rate decays by 0.1. For Kinetics-400, the whole training process becomes 100 epochs. The learning rate also decays by 0.1 at epoch 50 and 75. We also fine tune pre-trained weights from Kinetics-400 on HMDB51 and UCF101. The training epochs are scaled by half (20 epochs) in the transfer learning process. To avoid over-fitting, batch normalization layers are also frozen.The other training parameters are kept the same as before. In the testing process, a video is also partitioned equally into four segments, with each segment contributing one frame. We apply the same data augmentation (except RGB jittering) as described in the training process. We use the full resolution image with shorter side 256 for evaluation, in order to give a direct comparison. The only difference is that from each rescaled frame we do not crop only one patch. Instead, we follow [5] to extract ten patches by cropping and flipping four corners and the center of each frame. The final prediction score is obtained by averaging softmax scores from ten patches.