A custom ResNet architecture that uses One Cycle Policy to train the model
The model is trained on CIFAR-10 dataset using a custom Resnet architecture as shown below
Figure 1 : Resnet Architecture
The training is done using one cycle policy with following parameters
- Total Epochs = 24
- Max at Epoch = 5
- LRMIN = FIND
- LRMAX = FIND
- NO Annihilation
The following image augmentations are done using Albumentation Library
- RandomCrop 32, 32 (after padding of 4)
- FlipLR
- CutOut(8, 8)
The summary of the model is shown below
Figure 2 : Model summary
Result
The training logs, loss and accuracy can be found here
Figure 3 : Plot for loss and accuracy