Important Concepts of Transfer Learning
Pre-trained models: These are deep learning models that have been pre-trained on the large datasets such as ImageNet for vision tasks, and these pre-trained models can be used by developers in transfer learning.Fine-tuning: With this technique, the pre-trained model will still be used as a basis for re-training with a new dataset that employs a very small learning rate just to be able to transfer it to a new task.Feature extraction: The second approach is referred to as the fine-tuning approach, which involves using the pre-trained model as a fixed feature extractor; here only the final classification layer is replaced during training.Normalize(mean=[0.5], std=[0.5]): Normalize the tensor by mean subtraction and standard devision. This is because it normalizes the input data by subtracting the mean and dividing it by the standard deviation which will be able to increase the speed of model training.Transformer: Transforms are a key data preprocessing stage in computer vision tasks, thus allow transforming the input data to the suitable form and scale so that the model can be processed efficiently. These Transform operations prove to be effective for the imaging of computer vision tasks, especially for deep learning models training and testing on image datasets....