This project demonstrates object recognition using deep learning and the ResNet50 architecture. A pre trained convolutional neural network is applied to classify objects from the CIFAR dataset by learning high level visual patterns.
The project highlights the power of transfer learning and deep residual networks for image based classification tasks.
Project Objective
The objective of this project is to build an efficient object recognition system using ResNet50. Instead of training a deep neural network from scratch, a pre trained model is adapted to recognize multiple object categories with improved accuracy and reduced training time.
What This Project Does
The system processes color images and extracts meaningful visual features using the ResNet50 convolutional backbone. These features are then used to classify images into their respective object categories.
The workflow includes image preprocessing model training evaluation and prediction.
Dataset Description
The CIFAR dataset consists of small color images representing various object classes. Each image contains three color channels and captures objects under different backgrounds and visual conditions.
This dataset is widely used for benchmarking object recognition and image classification models.
ResNet50 and Transfer Learning
ResNet50 is a deep convolutional neural network that uses residual connections to enable stable training of deep architectures. These skip connections help the model learn complex patterns without suffering from vanishing gradient problems.
Transfer learning is applied by freezing the convolutional layers of the pre trained model and adding custom classification layers for the CIFAR dataset.
Model Architecture
The architecture includes the ResNet50 convolutional base followed by fully connected layers. Nonlinear activation functions enable the model to learn discriminative object features across multiple classes.
The final output layer produces probability scores for each object category.
Training and Evaluation
The model is trained using labeled image data with validation monitoring to ensure stable learning. After training the model is evaluated on unseen test images to assess classification performance and generalization capability.
Key Highlights
- Object recognition using deep residual networks
- Transfer learning with pre trained ResNet50
- Efficient preprocessing of CIFAR images
- Multi class image classification pipeline
- End to end deep learning workflow
Technologies Used
Python for implementation
TensorFlow and Keras for deep learning modeling
NumPy for numerical processing
Pre trained ResNet50 architecture
Matplotlib for visualization
Use Case and Applications
This project demonstrates real world object recognition techniques used in computer vision systems such as autonomous vehicles image search content moderation and smart surveillance.
The approach can be extended to larger datasets and more complex classification tasks.