rishika-afk/rcnn-crop-weed-detection

This project aims to optimize the performance of an RCNN (Region-based Convolutional Neural Network) model for object detection using Bayesian optimization and Crow Search Optimization (CSO), Logistic Regression.

RCNN Optimization Project

Project Description

This project focuses on optimizing the performance of an RCNN (Region-based Convolutional Neural Network) model for object detection tasks. Leveraging advanced optimization techniques such as Bayesian Optimization and Crow Search Optimization (CSO), the aim is to fine-tune hyperparameters and enhance the model's accuracy and efficiency in detecting objects within images. The project utilizes a dataset sourced from Kaggle, providing a diverse collection of annotated images for training and testing the RCNN model.

Dataset

The dataset used in this project is sourced from Kaggle and comprises a diverse collection of annotated images suitable for training and evaluating object detection models. It provides a rich variety of objects in different settings, enabling comprehensive training and testing of the RCNN model.

Link to Dataset on Kaggle

Requirements

To replicate and run this project, ensure you have the following dependencies installed:

• Python
• Libraries: NumPy, scikit-learn, TensorFlow, Keras, CSO (Crow Search Optimization library), scikit-optimize

Instructions

1. Download the dataset from the provided Kaggle link.
2. Preprocess the dataset according to your requirements.
3. Run the optimization scripts (bayesian_optimization.py, cso_optimization.py) to fine-tune the RCNN model's hyperparameters.
4. Train the RCNN model using the optimized hyperparameters.
5. Evaluate the trained model's performance on the test dataset.
6. Experiment with different hyperparameters and optimization techniques to further improve model performance.

References

• Link to RCNN
• Link to scikit-optimize Documentation