Inception V3 Image Processing (1).pptx

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1. Inception Network Image Processing Ahmed Farag Fawzy 201801203 Ammar Yasser Salah 201900403 Mahomoud Mohamed Abd ElBasir 201901402 Eslam Mohamed Hassan Ahmed Hassan 201801230 Asser Walid Ahmed 201900882 Supervised By: Dr. Wissam Salama

2. Introduction This presentation introduces Inception v3 model of I m a g e P r o c e ss i n g , a p o p u l a r D ee p l e a r n i n g m o d e l developed by Google. It outlines the features and c a p a b ili t i e s o f I n c e p t i o n v 3 , w h i c h h a s b ee n w i d e l y used in many high-level tasks, demonstrating its p o t e n t i a l a n d e ff e c t i v e n e ss .

3. Problem Statement Dogs and Cats have similar features which make it difficult to classify a picture whether it is a dog or cat.

4. Objective Our goal is to develop a Model that can distinguish between an image of a dog or a cat.

5. Dataset Used Type of data in our dataset is .JPG images of dogs and cats. The size of the dataset is 10000 image divides into 5000 for cats and dogs each (training=4000, testing=1000, validation=1 for each). The number of classes is 2 (Binary classification)

6. Evolution of Inception Network An inception module consists of a set of convolutional layers with different filter sizes and a pooling layer, all concatenated together. Improved the design of the inception module by adding batch normalization and factorized convolutions. Inception-v3 uses RMSprop optimization, label smoothing regularization, and an auxiliary classifier to improve training

7. Dataset: type of data, size The Inception-v3 model is a variant of the original Inception model and was introduced by Google researchers in 2015. Like the original Inception model, the Inception-v3 model was also trained on the ImageNet dataset. The ImageNet dataset used to train the Inception-v3 model has the following characteristics: Type of data: The dataset consists of high-resolution RGB images with a size of 224x224 pixels. Size: The ImageNet dataset used to train the Inception-v3 model contains approximately 1.2 million images for training, 50,000 images for validation, and 100,000 images for testing.

8. Number of classes: The ImageNet dataset used to train the Inception-v3 model has 1,000 object categories. Each image in the dataset is labeled with a single object category, such as "cat," "dog," "car," etc. It's worth noting that the Inception-v3 model was also pre-trained on a dataset called JFT-300M before being fine-tuned on the ImageNet dataset. The JFT-300M dataset is a large-scale dataset with over 300 million images and 18,291 object categories. However, the JFT-300M dataset is not publicly available, and it was only used for pre-training the Inception-v3 model. Number Of Classes

9. H o w I n c e p t i o n v 3 W o r k s

10. Inception V3 Model Architecture The inception v3 model was released in the year 2015, it has a total of 42 layers and a lower error rate than its predecessors. Let's look at what are the different optimizations that make the inception V3 model better. The major modifications done on the Inception V3 model are Factorization into Smaller Convolutions Spatial Factorization into Asymmetric Convolutions Utility of Auxiliary Classifiers Efficient Grid Size Reduction

11. Applications The Inception-v3 model is a powerful deep neural network that has been widely used in various computer vision applications. Here are some of the applications of the Inception-v3 model: Image classification : The Inception-v3 model can be used to classify images into different object categories, such as animals, plants, vehicles, etc. It achieves state-of-the-art performance on the ImageNet dataset, which is a benchmark for image classification tasks. Medical image analysis : The Inception-v3 model can be used in medical image analysis applications, such as tumor detection in medical images, where it has been shown to achieve high accuracy.

12. The Inception-v3 model is a deep convolutional neural network that was designed and trained to perform image classification tasks. Its primary motivation is to improve the accuracy of image classification while minimizing the computational resources required for training and inference. Inception-v3 is a successor to the earlier Inception models and incorporates several key features such as: Factorized convolution Inception modules Auxiliary classifiers By combining these and other techniques, Inception-v3 achieves state-of-the-art performance on several image classification benchmarks while being more computationally efficient than previous models. Motivation of Inception-v3 model

13. Benefits of Inception v3 Inception v3 is the most accurate and efkcient m o d e l f o r p r o c e ss i n g i n p u t i m a g e s c o m p a r e d t o other existing models. It is being constantly i m p r o v e d a n d i s a b l e t o r e c o g n i z e s p e c i k c p a tt e r n s , features and images with more efkciency. The model is even able to identify objects that can be seen in a single frame as well as dynamic objects. This makes it suitable for a variety of image processing tasks, ranging from facial recognition, product r e c o mm e n d a t i o n , a n d c l a ss i k c a t i o n t o r e a l - t i m e o b j e c t d e t e c t i o n .

14. D isadvantages of Inception v3 I n c e p t i o n v 3 a ll o w s u s t o s a v e s i g n i k c a n t l y m o r e computational power by using fewer resources. It c a n p r o c e s s i m a g e s m o r e q u i c k l y a n d a cc u r a t e l y compared to most other image processing models, a n d h a s g r e a t e r a cc u r a c y i n d e t e c t i n g a n d predicting facial features. This helps reduce errors and improve the quality of results. Furthermore, it’s easy to calibrate and optimize, allowing us to q u i c k l y i m p l e m e n t i t i n v a r i o u s e x i s t i n g applications.

15. Conclusion I n c e p t i o n v 3 m o d e l i s a h i g h l y a cc u r a t e , e f k c i e n t a n d r e li a b l e m o d e l fo r i m a g e p r o c e ss i n g . I t h e l p s t o simplify and streamline images processes, as well as reduce errors and improve the accuracy and quality of the results. It’s also easy to calibrate and optimize, making it an ideal choice for various projects.