definition.py

import torch.nn as nn

class FlowersImagesDetectionModel(nn.Module):
    def __init__(self, num_classes):
        super(FlowersImagesDetectionModel, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(128 * 28 * 28, 512)  # Adjust the input size according to your image size after resizing
        self.fc2 = nn.Linear(512, num_classes)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = self.pool(self.relu(self.conv3(x)))
        x = x.view(-1, 128 * 28 * 28)  # Adjust this according to the output size of the convolutional layers
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x