The model reaches a maximum accuracy of 91.11% on CIFAR-10 dataset using ResNet-18 model.
LR Finder and Reduce LR on Plateau was implemented for model training.
The model uses the library TensorNet to train the model. The library can be installed by running the following command
pip install torch-tensornet==0.0.7
The source code for the library can be found here.
- Loss Function: Cross Entropy Loss (combination of
nn.LogSoftmax
andnn.NLLLoss
) - LR Finder
- Start LR: 1e-7
- End LR: 5
- Number of iterations: 400
- Optimizer: SGD
- Momentum: 0.9
- Learning Rate: 0.012 (Obtained from LR Finder)
- Reduce LR on Plateau
- Decay factor: 0.1
- Patience: 2
- Min LR: 1e-4
- Batch Size: 64
- Epochs: 50
The following data augmentation techniques were applied to the dataset during training:
- Horizontal Flip
- Rotation
- CutOut
Some of the examples of GradCAM on misclassified images is shown below:
Install the required packages
$ pip install -r requirements.txt
Upload the files in the root folder and select Python 3 as the runtime type and GPU as the harware accelerator.
- Shantanu Acharya (Canvas ID: 25180630)
- Rakhee (Canvas ID: 25180625)