Fake image classification system using python
Making a fake image sorter with Python is like creating a pretend brain for pictures. We use Python tools like TensorFlow or PyTorch to make a simple thinking pattern. First, we make up a bunch of fake pictures showing different things. Then, we teach our pretend brain by showing these pictures and telling it what’s in each one. The brain learns by practicing and getting better. We test it to see how well it can guess. Even though it’s just a pretend game, it helps us understand how real picture-sorting systems work.
Advantages of using the system:
1. Enhances understanding of image classification concepts.
2. Provides hands-on experience in building neural networks.
3. Facilitates experimentation with different model architectures and parameters.
4.Helps grasp the training and evaluation processes in machine learning.
5.Offers insights into the challenges and considerations of real-world image classification systems.
Fake Image Identification using Machine Learning
Fake Image Classification Project using Machine Learning, this project developed using python programming language and graphical user interface designed in tkinter. Machine learning model is trained on large dataset of real and fake images, user have to write training script in python to train model and test script is to test images. This project have software framework and user have to provide input image to model and model will classify it as real or fake image.
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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