A WideResNet-based image-classification workflow covering the CNN architecture, training and evaluation on CIFAR data.
The problem
Deep image classification needs an integrated workflow, from loading data and implementing the architecture through to training and evaluation.
What was built
A PyTorch workflow that implements and trains a WideResNet architecture on CIFAR image data.
Technical approach
- Load CIFAR image data
- Build a residual WideResNet model
- Run the training workflow
- Evaluate how the model classifies images
- Inspect the output through verified files where available
Engineering decisions
- Hold back the architecture depth, width and training configuration until the notebook has been verified.
- Do not publish accuracy or hardware claims without genuine output.
- Present the project as practice in developing models, not as deployed vision software.
How it was checked
- The repository confirms CIFAR data, PyTorch and a WideResNet architecture.
What it shows
Hands-on implementation, training and evaluation of convolutional networks and residual architectures.
About the source
Academic and R&D project. The training configuration and the outcome metrics are unverified.