OptiVisionNet 0.3.0

Usage example: from OptiVisionNet.model import CNN_BiLSTM_MLP

OptiVisionNet

OptiVisionNet is a hybrid deep learning model designed for image classification tasks. It combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Multi-Layer Perceptrons (MLP) to achieve high performance on various image classification datasets.

Installation

To install OptiVisionNet, use pip:

pip install OptiVisionNet

Usage

from OptiVisionNet.model import CNN_BiLSTM_MLP from OptiVisionNet.utils import train_cnn_bilstm, evaluate_model from torchvision import datasets, transforms from torch.utils.data import DataLoader import torch.optim as optim import torch.nn as nn

Data Preparation

transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ])

train_data = datasets.CIFAR10(root="data", train=True, download=True, transform=transform) test_data = datasets.CIFAR10(root="data", train=False, download=True, transform=transform)

train_loader = DataLoader(train_data, batch_size=64, shuffle=True) test_loader = DataLoader(test_data, batch_size=64, shuffle=False)

Initialize Model

model = CNN_BiLSTM_MLP(input_channels=3, lstm_hidden_size=128, lstm_layers=2, output_size=10)

Train CNN + BiLSTM

criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) train_cnn_bilstm(model, train_loader, criterion, optimizer, epochs=5)

Evaluate the Model

accuracy, f1 = evaluate_model(model, test_loader) print(f"Test Accuracy: {accuracy:.2f}%") print(f"Test F1 Score: {f1:.2f}")