Welcome to VisionArchitect, a repository dedicated to crafting and testing advanced CNN architectures using Keras and PyTorch. Here, you'll find a collection of Jupyter Notebooks focusing on various advanced convolutional neural network (CNN) models, along with sample images for testing purposes.

The primary goal of this repository is to provide a platform for experimenting with and evaluating different advanced CNN architectures. Whether you're interested in exploring cutting-edge models like ResNet, DenseNet, or Inception, or leveraging pre-trained networks for your projects, VisionArchitect offers a range of resources to support your journey in advanced computer vision.

• imageNetClasses.json: JSON file containing the classes from the ImageNet dataset.
• letNetAndAlexNetWithKeras.ipynb: Jupyter Notebook demonstrating the implementation of LeNet and AlexNet using Keras.
• PretrainNetworkPytorch.ipynb: Jupyter Notebook showcasing the usage of pre-trained networks in PyTorch.
• rankNAccuracyPytorch.ipynb: Jupyter Notebook for evaluating the accuracy of ranking in PyTorch.

1. Clone the Repository:

git clone https://github.com/your-username/VisionArchitect.git

Install Dependencies:

pip install -r requirements.txt

You can download the imageNetClasses.json file: [https://github.com/saeidKhoobdell/VisionArchitect/blob/main/imageNetClasses.json].

Contribution Contributions are welcome! If you have any ideas for improving existing models, implementing new architectures, or enhancing documentation, feel free to open an issue or submit a pull request.