blip-image-captioning-base-chestxray-finetuned

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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]

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• 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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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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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:

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 presented in Lacoste et al. (2019).

• 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