Welcome to the CIFAR-10 Object Detection Repository! This repository contains code and resources for training and evaluating object detection models on the CIFAR-10 dataset using a Convolutional Neural Network (CNN). The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The goal of this project is to develop and test CNN-based models capable of accurately detecting objects within these images.
- Introduction
- Prerequisites
- Usage
- Models
- Results
- Potential Improvement - Data Augmentation
- Contributing
- License
Identifying and localizing objects within an image is a fundamental computer vision task known as object detection. The widely used CIFAR-10 dataset serves as a benchmark for image classification tasks, but this repository ups the ante for object detection with CNN-based models. The CNN model on the CIFAR-10 dataset can be trained and tested using the code found in the CNN_object_prediction-Cifar10.ipynb Jupyter Notebook that is provided.
Before using this repository, ensure you have the following dependencies installed:
- Python (>= 3.6)
- TensorFlow (>= 2.x) or PyTorch (>= 1.6)
- NumPy
- Matplotlib
- OpenCV
To train the CNN object detection model on the CIFAR-10 dataset, open the CNN_object_detection-Cifar10.ipynb Jupyter Notebook and run the training code segment. Make sure to specify the desired number of epochs (e.g., 100 epochs) during training.
After training the CNN model, you can evaluate its performance on the test set using the same Jupyter Notebook. The evaluation code segment will compute the accuracy for the trained model.
The current implementation includes a CNN-based object detection model. However, feel free to explore and add more models to the repository to improve detection performance!
The results of the CNN model's performance are available in the Jupyter Notebook CNN_object_prediction-Cifar10.ipynb. The notebook will display the achieved accuracy and other evaluation metrics after training the model for 100 epochs.
To further improve the generalization and robustness of the object detection model, consider incorporating data augmentation techniques. To increase the diversity of the training set, data augmentation involves applying various transformations to the training data, such as rotations, flips, translations, and brightness adjustments. The model's performance on unobserved data can be improved by this augmentation, which can help it handle various object orientations, lighting situations, and viewpoints better.
Although data augmentation has not been used in the repository's current iteration, it offers a promising path for future development. Implementing data augmentation methods, like those offered by PyTorch's torchvision.transforms or TensorFlow's torchvision.generator, can be a beneficial addition to the training pipeline.
We welcome contributions to this repository! If you have any improvements, bug fixes, or new models to add, please open an issue or submit a pull request. Make sure to follow the existing coding style and include appropriate tests for any new functionality.
This project is licensed under the MIT License - see the LICENSE file for details.
Happy object detection!
Note: Make sure to update this README.md file as you add more features and improvements to your CIFAR-10 object detection repository. Include relevant images and example results in your repository to showcase the performance of your models.