I’ve just completed the coding and initial training of a Convolutional Neural Network (CNN) on my dataset. The model is designed for image classification and follows a standard architecture: several convolutional layers extract features from the images, followed by pooling layers to reduce dimensionality. The extracted features are then flattened and passed through fully connected layers, ending with a softmax output for classification.
The code handles data loading, preprocessing, model definition, training, and evaluation, and I’m now able to make predictions on new images with the trained model. The CNN gives a solid baseline, but for more complex datasets or higher accuracy, more advanced architectures can be beneficial.
My next step will be working with EfficientNet-B2, a state-of-the-art CNN model pre-trained on ImageNet. EfficientNet often outperforms standard CNNs because it scales depth, width, and resolution more efficiently, which usually results in better accuracy with fewer parameters. I’m excited to see how it improves the model’s performance on my dataset.
Stay tuned for updates on the EfficientNet training and results!
Top comments (0)