# Text classification using BLIP2

**URL:** <https://discuss.huggingface.co/t/text-classification-using-blip2/100978>\
**Category:** Beginners\
**Created:** [August 5, 2024, 2:23pm UTC](https://discuss.huggingface.co/t/text-classification-using-blip2/100978 "2024-08-05T14:23:51Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![atomwalk12](https://avatars.discourse-cdn.com/v4/letter/a/aca169/32.png) [@atomwalk12](https://discuss.huggingface.co/u/atomwalk12)\
**Post date:** [August 5, 2024, 2:23pm UTC](https://discuss.huggingface.co/t/text-classification-using-blip2/100978/1 "2024-08-05T14:23:51Z")

</div>

Hi everyone!

I was wondering whether my approach to the following problem is correct. I am trying to use the BLIP-2 model to perform classification on a small dataset.

My approach is the following:

1. Run the prompts and images through the model (using `Blip2ForConditionalGeneration`)
2. Retrieve the q-former last hidden state
3. Create a linear layer and map the information to a intermediary dimension (say 1024).
4. Finally map the intermediary dimension using a classification layer based on the number of classes in the problem.

Does this look fine? Below is a simplified version of my approach:

```Python
    def forward(self, data, labels):
        out = self.base_model(**data, output_hidden_states=True)
        
        # resize the features
        features = out.qformer_outputs.last_hidden_state.view(batch_size, -1)
        
        # mapping of the features to a intermediary dimension (e.g. 1024)
        x = self.fusion(features)

        # use the classifier
        logits = self.classifier(x)
       
        # apply cross entropy loss
        self.criterion(logits, labels)

```

As a note regarding step 1 above, as far as my understanding goes, the  
`q-former` is the one that synthesis the information received from the image encoder and the textual information received in the prompt ([according to this article](https://wandb.ai/gladiator/BLIP-2/reports/BLIP-2-A-new-Visual-Language-Model-by-Salesforce--VmlldzozNjM0NjYz#:~:text=Specifically%2C%20Q%2DFormer%20is%20a,to%20output%20the%20desired%20text.)). As a result, the output of the vision model can be ignored, since it was already processed by the q-former. I would be grateful for some feedback.