Hot-dog-Not-hot-dog-Classifier
A binary classifier to test whether an image belongs to the "hot dog" class or the "not hot dog" class, as seen on HBO's Silicon Valley.
Hot Dog Classification with Transfer Learning
This project uses transfer learning to build a binary classifier that can distinguish between images of hot dogs and images of "not hot dogs." Four different pre-trained models are used: InceptionV3, MobileNetV2, ResNet50, and VGG16. (See below for paper references.)
The Hot dog - Not hot dog Kaggle dataset is used for training and validation.
The pre-trained models are loaded with weights from the ImageNet dataset and fine-tuned on the training data. Initially, all layers of the base model are frozen, meaning their weights are not updated during training. After an initial training phase of 5 epochs, however, the base model is set to be trainable and fine-tuned (from layer 0 onward) for an additional 5 epochs with a lower learning rate. (For reference, please see the notebook directory to find my notebook for the MobileNetV2 model.)
After training, the models achieve the following validation accuracies:
| Model | Validation accuracy (after 5 epochs with pre-trained ImageNet weights) | Validation accuracy (after 5 epochs with fine-tuning from layer 0 onward) |
|---|---|---|
| InceptionV3 | 0.9397 | 0.9397 |
| MobileNetV2 | 0.9095 | 0.9146 |
| ResNet50 | 0.8894 | 0.9045 |
| VGG16 | 0.8141 | 0.8141 |
Overall, it seems that the InceptionV3 model has the best performance. Hot dog!
Paper references: