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. • I n t r o d u c t i o n
This presentation introduces Inception v3 model of
Image Processing,a popular Deep learningmodel
developed by Google. It outlines the features and
capabilities of Inception v3,which has been widely
used in many high-level tasks, demonstrating its
potential and effectiveness.

3. P r o b l e m S t a t e m e n t
 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. •How Inception v3 Works

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

12. • 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:
1. 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.
2. 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.

13. 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:
1. Factorized convolution
2. Inception modules
3. 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

14. •Benefits of Inception v3
Inception v3 is the most accurate and efkcient
model for processing input images compared to
other existing models. It is being constantly
improved and is able to recognize specikc patterns,
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
recommendation,and classikcation to real-time
object detection.

15. •Disadvantages of Inception v3
Inception v3 allows us to save signikcantly more
computational power by using fewer resources. It
can process images more quickly and accurately
compared to most other image processing models,
and has greater accuracy in detectingand
predicting facial features. This helps reduce errors
and improve the quality of results. Furthermore,
it’s easy to calibrate and optimize, allowing us to
quickly implement it in various existing
applications.

16. • C o n c l u s i o n
Inception v3 model is a highly accurate,efkcient
and reliable model for image processing.It helps to
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.