Transfer Learning is a machine learning technique where a model trained on one task is adapted or fine-tuned to perform a different, but related, task. While working on real-time problems and having very little data to train a DCNN model from scratch we select a pretrained model which has already been trained on a large amount of data and fine-tune the model to perform well on our dataset.
For Example, we are freelancing and the first project we got was from a breeder, it was to train a model to identify Cats and Dog in a data frame, we developed the model with the help of a large amount of dataset available. Now, we took up another project for a farmer and have to train another model we take the model we trained for the cats and dogs and fine-tune it by feeding our dataset to work well for Cows and Horses as well This is how Transfer Learning works.
The process typically involves taking a pre-trained model, removing the final layer or layers responsible for the specific task it was originally trained on, and then adding new layers that are tailored to the new task. This fine-tuning process helps the model adapt its learned representations to the nuances of the target problem.
ResNet-50 is a deep convolutional neural network architecture that has proven to be highly effective in various computer vision tasks. It's known for its depth and skip connections, which allow for the training of very deep networks without encountering the vanishing gradient problem. These skip connections make it easier for the network to learn and retain important features from the data.
The CIFAR-10 dataset, on the other hand, is a popular benchmark dataset for image classification tasks. It contains 60,000 32x32 color images across 10 different classes, with 6,000 images per class. The goal is to correctly classify these images into their respective categories, such as airplanes, automobiles, birds, cats, and more.
By applying the ResNet-50 model to the CIFAR-10 dataset, we aim to leverage the power of transfer learning. The pre-trained ResNet-50 model, which has learned rich image features from a large dataset, can be fine-tuned to perform the specific task of classifying CIFAR-10 images. This approach can potentially yield state-of-the-art results on the CIFAR-10 dataset, thanks to the generalization capabilities of the ResNet-50 architecture.
Overall, this repository provides an example of how to harness the capabilities of ResNet-50 for image classification tasks using the CIFAR-10 dataset as a testbed.
RESNET50 ARCHITECTURE