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Model Description
This is the model card of a ๐ค 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]
- 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
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
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
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