This project uses a Convolutional Neural Network (CNN) to predict the coordinates (x, y) of a pixel with a value of 255 in a 50x50 grayscale image where all other pixels have a value of 0.
The dataset is generated synthetically by randomly assigning the pixel with a value of 255 in each 50x50 image.
A CNN model is built using TensorFlow and Keras. The model predicts the coordinates of the pixel with value 255. Model consist of First Convolutional Layer followed by Max Pooling Layer and Second Convolutional Layer followed by Max Pooling Layer then Flatten Layer. For more info refer 'Model.cfg' file
The model is trained on a dataset of 10,000 samples with an 80-20 train-test split.
The model's performance is evaluated, and predictions are visualized to compare the ground truth coordinates with the predicted coordinates.
- tensorflow==2.9.0
- numpy==1.23.0
- matplotlib==3.5.2
- scikit-learn==1.1.1
To install the dependencies, run:
pip install -r requirements.txt