https://github.com/arpanpramanik2003/object-detection-resnet50

This repository contains a deep learning project for CIFAR-10 image classification using the ResNet50 pre-trained model. The project includes data preprocessing, model training, evaluation, and visualization of results. Achieved high accuracy by fine-tuning the model and optimizing hyperparameters.
https://github.com/arpanpramanik2003/object-detection-resnet50

cifar-10 cifar10 cnn deep-learning keras machine-learning model-evaluation object-detection opencv pre-trained-model python regression-models resnet-50 streamlit tensorflow2 transformer-models

Last synced: 7 months ago
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This repository contains a deep learning project for CIFAR-10 image classification using the ResNet50 pre-trained model. The project includes data preprocessing, model training, evaluation, and visualization of results. Achieved high accuracy by fine-tuning the model and optimizing hyperparameters.

• Host: GitHub
• URL: https://github.com/arpanpramanik2003/object-detection-resnet50
• Owner: arpanpramanik2003
• License: apache-2.0
• Created: 2025-01-20T08:27:52.000Z (9 months ago)
• Default Branch: master
• Last Pushed: 2025-03-11T18:14:24.000Z (7 months ago)
• Last Synced: 2025-03-11T19:27:03.839Z (7 months ago)
• Topics: cifar-10, cifar10, cnn, deep-learning, keras, machine-learning, model-evaluation, object-detection, opencv, pre-trained-model, python, regression-models, resnet-50, streamlit, tensorflow2, transformer-models
• Language: Jupyter Notebook
• Homepage:
• Size: 192 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# CIFAR-10 Image Classification using ResNet50

## Introduction

This project focuses on classifying images from the CIFAR-10 dataset using a deep learning model based on the ResNet50 architecture. The CIFAR-10 dataset consists of 50,000 32x32 color images in 10 different classes, with 5,000 images per class. Leveraging the power of the pre-trained ResNet50 model, the project aims to achieve high accuracy through transfer learning.

## Project Overview

The steps involved in the project are as follows:

1. **Dataset Preparation**

   - Download the CIFAR-10 dataset using Kaggle API.

   - Extract the dataset and perform necessary preprocessing.

   - Apply One-Hot Encoding to the labels.

2. **Data Preprocessing**

   - Load image data and convert them into numpy arrays.

   - Normalize pixel values by scaling them to the range [0,1].

   - Split the dataset into training and testing sets.

3. **Model Development**

   - Utilize the ResNet50 pre-trained model with `imagenet` weights.

   - Add additional layers such as upsampling, dense, dropout, and batch normalization.

   - Compile the model using RMSprop optimizer and categorical crossentropy loss function.

4. **Model Training and Evaluation**

   - Train the model with the training dataset and validate using a validation split.

   - Evaluate model performance using accuracy and loss metrics.

5. **Results Visualization**

   - Plot training vs validation loss.

   - Plot training vs validation accuracy.

## Requirements

To run this project, you need the following dependencies:

```bash

pip install numpy pandas matplotlib tensorflow keras opencv-python PIL scikit-learn kaggle

```

## Execution Steps

1. Ensure you have the `kaggle.json` file configured to download the dataset.

2. Run the Python script to download, preprocess, train, and evaluate the model.

3. Observe the evaluation results and plots to analyze model performance.

## Model Performance

The model achieved a good test accuracy, showing the effectiveness of transfer learning using ResNet50. Further improvements can be made by tuning hyperparameters and using data augmentation techniques.

## Conclusion

This project demonstrates the application of deep learning for image classification using pre-trained models. ResNet50, with its powerful feature extraction capabilities, enhances the classification accuracy, making it suitable for practical applications.

## Author

Arpan Pramanik

## Acknowledgments

- Kaggle for the CIFAR-10 dataset.

- TensorFlow and Keras libraries for deep learning implementation.

## License

This project is licensed under the Apache License.