How you approach this task depends what model or framework you are using but generally sharing parameters is a good idea. what I recommend you do is something like this (what im putting below is a rough outline throw it in chat gpt and it will get it) the idea being that you can separate segmentation and categorization to make the job of making a dataset easier for yourself. You should be able to pass in even differently sized batches of Segmentation data and Categorization data without issue.: import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F

class MultiTaskModel(nn.Module): def init(self, num_layers, hidden_dim, num_categories, image_size): super(MultiTaskModel, self).init() # Define shared processing layers, ideally in a U-Net for segmentation tasks self.processing_layers = nn.ModuleList([nn.LazyLinear(hidden_dim) for _ in range(num_layers)])

    # Task-specific heads for classification and segmentation
    self.category_head = nn.LazyLinear(num_categories)
    self.segmentation_head = nn.LazyLinear(image_size)  # if you use a conv layer as the output you can have different segmentation channels using the number of filters
    
    # Optimizer setup
    self.optimizer = optim.Adam(self.parameters(), lr=3e-4)

def forward(self, x):
    x = torch.flatten(x.float(), start_dim=1)
    for layer in self.processing_layers:
        x = F.relu(layer(x))
    categories = self.category_head(x)
    segments = self.segmentation_head(x)
    return categories, segments

def train_step(self, Images1, Categories, Images2, Segments):
    self.optimizer.zero_grad()

    # Category prediction and loss calculation
    pred_categories, _ = self(Images1)
    categories_loss = F.cross_entropy(pred_categories, Categories)

    # Segmentation prediction and loss calculation
    _, pred_segments = self(Images2)
    segmentation_loss = F.binary_cross_entropy_with_logits(pred_segments, Segments)

    # Combined loss and backpropagation
    loss = categories_loss + segmentation_loss
    loss.backward()
    self.optimizer.step()