priyansu-bhandari / flower_and_plant_classification_using_cnn_model

The Flower and Plant Classification project is a deep learning application that utilizes a CNN model to accurately classify different types of flowers and plants based on their images.

Flower_and_Plant_Classification_Using_CNN_Model

Description:

The Flower and Plant Classification project is a deep learning application that employs a Convolutional Neural Network (CNN) model to classify different types of flowers and plants. The project aims to provide an accurate and automated solution for identifying and categorizing various botanical species based on their images.

Features:

Image classification: The CNN model is trained to classify flower and plant images into predefined categories. Deep learning: The project leverages the power of convolutional neural networks to learn and extract meaningful features from input images. Pretrained models: The application utilizes pre-trained CNN models, such as VGG16 or ResNet, to achieve high accuracy in classification tasks. Training and evaluation: The project provides scripts to train the CNN model on a labeled dataset and evaluate its performance. Model visualization: The application includes functionality to visualize and analyze the CNN model's architecture and layer activations.

Installation:

Clone the repository: git clone https://github.com/your-username/flower-plant-classification.git

Install the required dependencies:

pip install -r requirements.txt

Usage:

Prepare the dataset: Ensure that your flower and plant images are organized in the correct folder structure, with each category placed in its respective subfolder.

Train the model: Use the train.py script to train the CNN model on your dataset. Specify the dataset directory and the desired model architecture (e.g., resnet50) as command-line arguments.

Evaluate the model: Evaluate the trained model's performance using the evaluate.py script. This will provide accuracy metrics and classification results on a test dataset.

Classify new images: Use the classify_image.py script to classify new flower and plant images. Provide the path to the image file as a command-line argument, and the model will predict the corresponding category.

Dependencies:

Python (3.6+)

TensorFlow or PyTorch (based on the chosen CNN framework)

Keras or TorchVision (for pretrained models)

NumPy

Matplotlib

OpenCV