Answers.Body: <p>Look at CLIP, it's somewhat relevant. The model can is capable of classifying a non-fixed number of classes</p>
  
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Answers.Text: I thought so as well. If you use an embedding model you can always use kNN for classification
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Answers.Text: I will use CLIP next currently working with pretrained models and metric learning. 
  
  I am getting issue with embeddings because I have classes which are very similar and they don't have proper clusters. So, classification using kNN won't work. @Ggjj11
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Answers.Body: <p>If you have a lot of classes that can be very similar (or not), you could maybe take a look at margin losses such as Arcface of Cosface.</p>
  <p>Also start from a very well pretrained foundation model such as DINOv2 and fit a small classifier (even a simple linear layer could work decently) on top of its features.</p>
  
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Answers.Text: Yes, I am working with pretrained models for classification and also looking into margin losses such as Triplet loss.
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Body: <p>I am working on a problem that requires the classification of more than 60k classes. I have around 1k to 1.5k images per class. I am using synthetic data for training and want to evaluate it on real data. I have enough computing power but want to keep it computationally efficient and highly accurate (the tradeoff can be further adjusted).</p>
  <p>Currently, I am looking for papers in this direction. All papers mostly work with ImageNet 1k. I have a few things in my mind. I am considering starting with EfficientNet for supervised learning. I am also looking into Hierarchical classification and similarity matching by generating embeddings in multidimensional space.</p>
  <p>The data does not have a hierarchy. But I am also looking into it if I could somehow use it in hierarchies.</p>
  <p>I want suggestions on this. What methodology is best for it? or if there are any good papers.</p>
  
Tags: <neural-networks><deep-learning><image-recognition>
Title: Image classification of more than 60,000 classes
UserDisplayName: pks
ThreadText: # Image classification of more than 60,000 classes
  
  (Asked by: username_0 on 2024-02-16)
  
  I am working on a problem that requires the classification of more than 60k classes. I have around 1k to 1.5k images per class. I am using synthetic data for training and want to evaluate it on real data. I have enough computing power but want to keep it computationally efficient and highly accurate (the tradeoff can be further adjusted).
  
  Currently, I am looking for papers in this direction. All papers mostly work with ImageNet 1k. I have a few things in my mind. I am considering starting with EfficientNet for supervised learning. I am also looking into Hierarchical classification and similarity matching by generating embeddings in multidimensional space.
  
  The data does not have a hierarchy. But I am also looking into it if I could somehow use it in hierarchies.
  
  I want suggestions on this. What methodology is best for it? or if there are any good papers.
  
  **Tags:** neural-networks, deep-learning, image-recognition
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2024-02-23. Score: 1
  
  Look at CLIP, it's somewhat relevant. The model can is capable of classifying a non-fixed number of classes
  
  > **Comments:**
  > **username_2**: I thought so as well. If you use an embedding model you can always use kNN for classification  
  > **username_0**: I will use CLIP next currently working with pretrained models and metric learning.   I am getting issue with embeddings because I have classes which are very similar and they don't have proper clusters. So, classification using kNN won't work. @Ggjj11  
  
  ---
  
  ### Answer by username_3 on 2024-03-24. Score: 0
  
  If you have a lot of classes that can be very similar (or not), you could maybe take a look at margin losses such as Arcface of Cosface.
  
  Also start from a very well pretrained foundation model such as DINOv2 and fit a small classifier (even a simple linear layer could work decently) on top of its features.
  
  > **Comments:**
  > **username_0**: Yes, I am working with pretrained models for classification and also looking into margin losses such as Triplet loss.  
  
  ---
  
  