<p>Look at CLIP, it's somewhat relevant. The model can is capable of classifying a non-fixed number of classes</p>

CC BY-SA 4.0
2024-03-24T18:36:35.843Z
I thought so as well. If you use an embedding model you can always use kNN for classification
Ggjj11
CC BY-SA 4.0
2024-04-19T06:59:18.417Z
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
pks
CC BY-SA 4.0
2024-02-23T01:14:14.210Z
2024-02-23T01:14:14.210Z
JobHunter69
<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>

CC BY-SA 4.0
2024-04-19T06:55:30.957Z
Yes, I am working with pretrained models for classification and also looking into margin losses such as Triplet loss.
pks
CC BY-SA 4.0
2024-03-24T15:05:44.127Z
2024-03-24T15:05:44.127Z
Lelouch
<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>

<neural-networks><deep-learning><image-recognition>
Image classification of more than 60,000 classes
pks
# 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


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## 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. @username_2  

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### 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.  

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