This project implements a Convolutional AutoEncoder model for image compression using the CIFAR-10 dataset. The autoencoder is a type of unsupervised neural network that learns to compress (encode) images into a latent representation and then reconstructs (decodes) the images to their original form. The goal of this model is to demonstrate the effectiveness of deep learning in unsupervised image compression, with a focus on reducing the dimensionality of the images while preserving important features.
The model is designed to perform image compression by learning an efficient representation of the input images. The CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes, is used for training and evaluation. The architecture includes convolutional layers for feature extraction and upsampling layers for image reconstruction. This convolutional autoencoder demonstrates how deep learning can be leveraged for image compression tasks in a variety of applications, such as data storage and transmission.
- Image Compression: The autoencoder reduces the dimensionality of images while preserving key features for reconstruction.
- Unsupervised Learning: The model is trained without explicit labels, allowing it to learn efficient representations of the data.
- CIFAR-10 Dataset: A well-known dataset that contains 60,000 32x32 color images, divided into 10 classes (e.g., airplanes, cars, birds, etc.).
- Deep Learning: Uses a deep learning approach with convolutional layers and deconvolutional layers (also known as transposed convolutions) for encoding and decoding images.
The model consists of the following components:
- The encoder is made up of several convolutional layers that progressively downsample the input image. Each convolutional block is followed by a MaxPooling2D layer, which reduces the spatial dimensions of the image while increasing the depth of the feature maps. The encoder works by extracting features from the input images and compressing them into a smaller representation.
- Convolutional layers: Reduce the spatial dimensions of the input image.
- MaxPooling2D layers: Downsample the image progressively.
- After the encoding process, the feature maps are flattened into a dense vector, which represents the compressed version of the input image. This vector is the latent representation of the image.
- The decoder works by progressively upsampling the compressed feature maps to reconstruct the original image. It mirrors the encoder structure but uses Conv2DTranspose (also known as deconvolutional) layers to upsample the feature maps back to the original image size (32x32). The output layer uses a sigmoid activation function to ensure that the pixel values are within the range of 0 to 1.
- The model is trained using the mean squared error (MSE) loss function, which minimizes the difference between the original and reconstructed images.
- The training process uses the Adam optimizer, which is an efficient optimization algorithm suitable for deep learning tasks.
- The trained model is evaluated on the CIFAR-10 test set, and the reconstruction quality is analyzed.
- Compression Rate: The model achieves a notable compression ratio by reducing the dimensionality of the input images.
- Reconstruction Quality: The reconstructed images are similar to the original images, although some fine details may be lost due to the compression.
- Image Compression: The autoencoder can be applied to tasks requiring efficient image storage or transmission.
- Data Reduction: Useful in scenarios where large amounts of data need to be reduced for faster processing or storage.
- Feature Extraction: The model can be used to extract meaningful features from images for further machine learning tasks.
To run this project locally:
- Clone the repository:
git clone https://github.com/Abmstpha/DeepLearning.git cd DeepLearning