| | --- |
| | framework: pytorch |
| | model: EfficientNet-B1 |
| | dataset: CIFAR10 (restructured to have random upright and upside-down samples, with labels {0:'up', 1:'down'} |
| | imgSize: 224x224x3 |
| | --- |
| | |
| |
|
| | This repository contains model trained to predict orientation {0:'up', 1:'down'} of images. |
| | The model '.pkl' file contains a dictionary with the following keys: |
| |
|
| | ```python: |
| | 'optimizer': optimizer state dictionary |
| | 'scheduler': scheduler state dictionary |
| | 'model': model state dictionary |
| | 'epoch': checkpoint epoch |
| | ``` |
| |
|
| | The image size is 3x224x224. The model can be initialized as: |
| |
|
| | ```python: |
| | model = torchvision.models.efficientnet_b1(pretrained=True) |
| | |
| | in_features = model.classifier[1].in_features |
| | out_features = 2 |
| | |
| | model.classifier[1] = nn.Linear(in_features, out_features, bias=True) |
| | ``` |
