This project demonstrates image classification using neural networks. The neural network architecture used here is based on a convolutional neural network (CNN), which is well-suited for tasks involving images.
- CNN Architecture: Utilizes a deep CNN for image classification.
- Dataset: Uses [Dataset Name] for training and testing.
- Training: Details about how to train the model.
- Evaluation: Information on evaluating the model's performance.
- Usage: Instructions on how to use the trained model for predictions.
To run this project locally, follow these steps:
- Clone this repository.
- Install the dependencies listed in
requirements.txt:
To train the model, run the following command:
python train.py
## Evaluating the Model
python evaluate.py
## Making Predictions
# Example code snippet for making predictions
`from model import load_model, preprocess_image
model = load_model('path_to_model_checkpoint')
image = preprocess_image('path_to_image')
prediction = model.predict(image)
print(prediction)`
##Replace 'path_to_model_checkpoint' and 'path_to_image' with actual paths.