Answers.Body: <p>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</p>
  <p>class MultiTaskModel(nn.Module):
  def <strong>init</strong>(self, num_layers, hidden_dim, num_categories, image_size):
  super(MultiTaskModel, self).<strong>init</strong>()
  # 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)])</p>
  <pre><code>    # 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()
  </code></pre>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-11-06T20:35:49.393Z
Answers.LastActivityDate: 2024-11-06T20:35:49.393Z
Answers.UserDisplayName: Leonhard Piff
Body: <p>I am beginning to work on a non-linear navigation system for educational videos as a part of my undergrad coursework project. As a part of it, I need to classify the unique frames (frame which is slide, handwritten, only figure, with both text and figure, only text, blank frame) and also need to segment (here also I have a question, is it a segmentation task or should I go with multi-object detection) the content of the frame (text, figure, title, figure title). I would like to do this using a single model trained on a single custom dataset. But I am not sure about the practicality of this.</p>
  <p>I have some questions regarding this:</p>
  <ol>
  <li>I am not sure how I should prepare the dataset for this task.
  Whether I could use annotation &amp;its labels and class labels in the
  same dataset?</li>
  <li>Can I train the model on the dataset (if the answer to
  1 is yes) only once to achieve both tasks? Do I need to make any
  other changes to the model except some changes to the final layer?</li>
  </ol>
  <p>Thank you in advance!</p>
  
Tags: <computer-vision><object-detection><training-datasets>
Title: Is it possible to achieve both detection and image classification by training the model on the custom dataset?
UserDisplayName: keerthana s
ThreadText: # Is it possible to achieve both detection and image classification by training the model on the custom dataset?
  
  (Asked by: username_0 on 2024-11-06)
  
  I am beginning to work on a non-linear navigation system for educational videos as a part of my undergrad coursework project. As a part of it, I need to classify the unique frames (frame which is slide, handwritten, only figure, with both text and figure, only text, blank frame) and also need to segment (here also I have a question, is it a segmentation task or should I go with multi-object detection) the content of the frame (text, figure, title, figure title). I would like to do this using a single model trained on a single custom dataset. But I am not sure about the practicality of this.
  
  I have some questions regarding this:
  
  1. I am not sure how I should prepare the dataset for this task.
     Whether I could use annotation &its labels and class labels in the
     same dataset?
  2. Can I train the model on the dataset (if the answer to
     1 is yes) only once to achieve both tasks? Do I need to make any
     other changes to the model except some changes to the final layer?
  
  Thank you in advance!
  
  **Tags:** computer-vision, object-detection, training-datasets
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-11-06. Score: 0
  
  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()
  
  
  ---
  
  