<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>

CC BY-SA 4.0
2024-11-06T20:35:49.393Z
2024-11-06T20:35:49.393Z
Leonhard Piff
<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>

<computer-vision><object-detection><training-datasets>
Is it possible to achieve both detection and image classification by training the model on the custom dataset?
keerthana s
# 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()


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