Best Pre-Trained Model For Image Classification 2021

While there are many pre-trained Image Classification models, few of them can compete with Google’s Inception V3 model in speed and accuracy. So, what is Inception V3 exactly?

With a dedicated NVIDIA TITAN Xp for training and a new 4,000 USD AWS instance for inference, we were able to train one of the fastest and most accurate image classification models in the industry. Every two weeks we dedicate more time to fine-tuning our model and adding new features to it. We leave no stone unturned: from regularizing the memory footprint of our model and not letting it overfit on the dataset – to saving energy by parallelizing our GPU-based pre-trained model.

Have you ever used a pretrained model before? If not, I’ll explain what they are. If you have, however, I’ll talk about why they’re so popular in 2017.

Best Pre-Trained Model For Image Classification 2021

In our daily routine, we unknowingly perfectly transfer the knowledge of some activity or task to the related one. Whenever we come across a new problem statement or task, first we recognize it and try to apply the relevant experience which results in hassle-free completion of the task. Following this same approach, a term called Transfer Learning is used in the field of deep learning which facilitates the use of already trained models to the related applications. Here we are going to discuss in detail the transfer learning that is popularly used in the field of deep learning along with different models used in this domain along with a critical investigation of their features.

1 Xception

It translates to “Extreme Inception”. Proposed by the creator of Keras, this is an extension of the inception model and like MobileNet it has replaced the normal modules with depth wise separable convolution modules. But the catch here is that the convolutions are at the extreme end of the spectrum. With a size of 91MB, it is one of the smallest weighted models in the list. It works with Tensorflow framework only. By many experimental studies, it was shown that Xception showed optimally better results when compared to InceptionV3.

2 VGG16 and VGG19:
This is a keras model with 16 and 19 layer network that has an input size of 224X224. It was introduced by Visual Geometry Group of the University of Oxford. This model emerged as a result of the win for the ‘VGG team’ at a competition. It was further improvised and we got in the best-performing Image Classification results: the 16 layers and the 19 layer models namely VGG16 and VGG19. The two models are compatible with Keras and Caffe toolbox and are readily available repositories available for reference. The combined product of VGG16 and VGG19 is referred to as VGGNet. The famous publication “K. Simonyan, A. Zisserman Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv technical report, 2014” depicts the aims and objectives of this project.

3 ResNet50
The ResNet architecture is another pre-trained model highly useful in Residual Neural Networks. ResNet50 is the variant with 50 layers. In residual learning, the network learns the residuals of the input layer. It has shown that training residual networks are much easier than trying a Convoluted Neural Network (CNN). It is a result of the Microsoft team that won the ILSRVC competition in 2015. It has allowed the Deep Learning Scientists to create deeper layers and reducing vanishing gradients. For more understanding refer to the research paper: “Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research” .

4 InceptionV3
It can be easily implemented using Tensorflow and Keras. It is a huge scale image recognition system and can be used in transfer learning problems. With its rich feature representations, it is able to classify images into nearly 1000 object based categories. It is a 48 layer network with an input size of 299×299. It has been improved from the InceptionV2 and has installed upgrades like the new Label Smoothing, Factorized 7×7 convolutions etc.

5 DenseNet
DenseNet is relatively new and is considered to be the chronological extension of ResNet. It also introduces the Densely Connected Neural Networks that helps us to get deeper insights, efficient and accurate trainings and outputs. Like others, it also reduces the number of parameters and enables us to reuse the already initialized features. DenseNet is a simpler and effective model especially for feature concatenation when compared to other architectures.

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