At the heart of any AI based system there lies a trained model. Training a model is no small feat and requires an experienced eye to choose the right model for your needs. There are numerous models available, but only a few have stood the test of time and worked well across numerous applications ranging from industrial automation to medical diagnosis. In this article, we will look at the top 3 pre-trained models for image classification and see how they work.
Image classification is the problem of classifying the content of an image. This is very important for object recognition, face recognition and more. We will work with a pre trained model, resize it to tiny size and solve the problem of image classification easily.
I have an image processing task, on which I could get better results if I could use the pre trained ImageNet model – The problem is that I need to classify flowers in 50 classes with 3-4 flowers per class. So the size of color images will be 15000 x 15000 pixels. Which model would you recommend me to use?
Best Pre Trained Models For Image Classification
The human brain can easily recognize and distinguish the objects in an image. For instance, given the image of a cat and dog, within nanoseconds, we distinguish the two and our brain perceives this difference. In case a machine mimics this behavior, it is as close to Artificial Intelligence we can get. Subsequently, the field of Computer Vision aims to mimic the human vision system – and there have been numerous milestones that have broken the barriers in this regard.
Moreover, nowadays machines can easily distinguish between different images, detect objects and faces, and even generate images of people who don’t exist! Fascinating, isn’t it? One of my first experiences when starting with Computer Vision was the task of Image Classification. This very ability of a machine to distinguish between objects leads to more avenues of research – like distinguishing between people.
3 Pre-trained Image Classification Models
Tan and Le, 2019 first proposed the idea of EfficientNet. EfficientNet is one of the most efficient models with high levels of accuracy.
EfficientNet helps us form features of images and pass them to a classifier. This makes EfficientNet the backbone of many classification tasks.
EfficientNet provides a class of models, from B1 to B7, based on B0 as the baseline model. The course presents different accuracy and efficiency levels at various scales.
While the EfficientNet-B0 variant of the model contains 237 layers, EfficientNet-B7 has a total of 813 layers. They obtain an incredible level of performance on the CIFAR-100 and ImageNet datasets.
EfficientNet models work better for complex tasks and are more efficient than competitors.
VGG-16 is a popular image classification model developed at the University of Oxford.
Though it rolled out at the famous ILSVRC 2014 Conference, the model remains unbeatable today.
Back then, VGG-16 made it to the top of the standard of AlexNet and won the classification challenge at an accuracy level of 92.7 percent.
In response, the researchers and the industry were fascinated by the model and quickly adopted it for image classification tasks.
VGG-16 is a 16 layers-deep convolutional network net (CNN) architecture. Its pre-trained model has learned rich feature representations of over a million images.
It contains 1000 object categories such as a mouse, keyboard, pencil, or several animals to classify images. The network takes image input sizes 224×224 and converts them into features.
The 50 layers-deep convolutional network, ResNet50, is a powerful model for various image classification tasks.
1000s of images used for preparing the model are taken from the ImageNet database. The model is based on more than 23 million parameters, making it better for image classification.
The model involves skipping connection, directly combining the previous layer’s input with another layer’s output. Thus, it beats the limitation of VGG-16 by solving the issue of diminishing gradient, which makes it hard to train models.
ResNet50 can perform recognition tasks with fewer error rates.
Doing amazing things with visual data does not always need to be difficult.
We have a variety of computer vision tasks such as instance segmentation, object detection, image processing, and image classification that help us derive insights from image data and make recommendations for real-life scenarios.
We do not need to collect huge datasets and build models from scratch to perform these tasks.
We can use models like ResNet50, VGG-16, or EfficientNet, which are already trained on huge datasets and perform classification at different accuracy levels.