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Image Classification using Transfer Learning in Pytorch
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In this video we will discuss how to implement Image Classification using transfer learning in pytorch. We will be using RESNET model.
We use transfer learning to use the low level image features like edges, textures etc. learnt by a pretrained model, ResNet50, and then train our classifier to learn the higher level details in our dataset images . ResNet50 has already been trained on ImageNet with millions of images.
Data Augmentation
The images in the available training set can be modified in a number of ways to incorporate more variations in the training process, so that the trained model gets more generalized and performs well on different kinds of test data. Also the input data can come in a variety of sizes. They need to be normalized to a fixed size and format before batches of data are used together for training.
Each of the input images are first passed through a number of transformations. We try to insert some variations by introducing some randomness into the transformations. In each epoch, a single set of transformations are applied to each image. When we train for multiple epochs, the models gets to see more variations of the input images with a new randomized variation of the transformation in each epoch. This results in data augmentation and the model then tries to generalize more.
ResNet50 is one of those having a good tradeoff between accuracy and inference time. When a model is loaded in PyTorch, all its parameters have their ‘requires_grad‘ field set to true by default. That means each and every change to the parameter values will be stored in order to be used in the back propagation graph used for training. This increases memory requirements. So, since most of the parameters in our pre-trained model are already trained for us, we reset the requires_grad field to false.
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We use transfer learning to use the low level image features like edges, textures etc. learnt by a pretrained model, ResNet50, and then train our classifier to learn the higher level details in our dataset images . ResNet50 has already been trained on ImageNet with millions of images.
Data Augmentation
The images in the available training set can be modified in a number of ways to incorporate more variations in the training process, so that the trained model gets more generalized and performs well on different kinds of test data. Also the input data can come in a variety of sizes. They need to be normalized to a fixed size and format before batches of data are used together for training.
Each of the input images are first passed through a number of transformations. We try to insert some variations by introducing some randomness into the transformations. In each epoch, a single set of transformations are applied to each image. When we train for multiple epochs, the models gets to see more variations of the input images with a new randomized variation of the transformation in each epoch. This results in data augmentation and the model then tries to generalize more.
ResNet50 is one of those having a good tradeoff between accuracy and inference time. When a model is loaded in PyTorch, all its parameters have their ‘requires_grad‘ field set to true by default. That means each and every change to the parameter values will be stored in order to be used in the back propagation graph used for training. This increases memory requirements. So, since most of the parameters in our pre-trained model are already trained for us, we reset the requires_grad field to false.
We also provide consulting services for data analytics / ml /deep learning to help grow companies. Contact us at below email id
HELP US MAKE MORE SUCH VIDEOS AND OPEN A WONDERFUL 🎓 SCHOOL!
DONATE to make this channel study centre and School
Name : Geeta Gupta [She is my mother]
Account Number : 00000031796817390
Bank : State bank of india
Branch : Meston Road,Kanpur,Uttar Pradesh ,India
IFSC Code : SBIN0001790
City : Kanpur
MICR CODE :208002023
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