---
library_name: transformers
tags:
- medical
license: bsd-3-clause
language:
- en
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Umar Igan
- **Model type:** VLM
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Salesforce/blip-image-captioning-base

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]

## Uses

This is a fine-tuned VLM on chest xray medicald dataset, the result shouldn't be used as an advice!!
### Direct Use

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[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Example usage:

```python
from transformers import BlipForConditionalGeneration, AutoProcessor

model = BlipForConditionalGeneration.from_pretrained("umarigan/blip-image-captioning-base-chestxray-finetuned").to(device)
processor = AutoProcessor.from_pretrained("umarigan/blip-image-captioning-base-chestxray-finetuned")

inputs = processor(images=image, return_tensors="pt").to(device)
pixel_values = inputs.pixel_values

generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_caption)
```
### Training Data

https://huggingface.co/datasets/Shrey-1329/cxiu_hf_dataset

#### Training Hyperparameters

- lr: 5e-5
- Epoch: 10
- Dataset size: 1k
#### Summary
A simple blip fine-tuned model on medical imaging

## Environmental Impact

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** GPU
- **Hours used:** 1
- **Cloud Provider:** Google
- **Compute Region:** Frankfurt
- **Carbon Emitted:** 

### Compute Infrastructure

Google Colab L4 GPU

#### Hardware

Google Colab L4 GPU


## Model Card Contact

Umar Igan