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Training a Vision-Based LLM to Detect Pneumonia with Hugging Face: My Journey

  • Roland
  • Jun 12
  • 3 min read
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Training an X-Ray Vision AI to Detect Pneumonia — My Hugging Face Experience

Last year, I embarked on an exciting project to build a vision-based large language model (LLM) using a large dataset of chest X-ray images. The goal? Train an AI model capable of detecting pneumonia with high accuracy. I’m now publicly make this model available to everyone — and it’s performing with up to 95% accuracy.


You can check it out here:

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What is Hugging Face?

For those unfamiliar, Hugging Face is a powerful platform and community built around open-source AI. While it's often known for hosting large-scale NLP models like BERT, GPT, and BLOOM, it also supports vision, audio, and multimodal projects. More importantly, it makes it incredibly easy for developers and researchers to share models, datasets, and training logs — making AI more accessible and collaborative.


Renting a GPU to Train Vision Models

Training large deep learning models on medical images is computationally intensive. That’s where Hugging Face’s Inference Endpoints and AutoTrain tools come in. But for full customization and power, I used Hugging Face Spaces + rented GPUs through their Accelerate and Trainer frameworks. They let you:

  • Spin up GPU-powered environments

  • Use preconfigured tools like PyTorch or TensorFlow

  • Scale training with minimal setup

  • Visualize progress via built-in TensorBoard

If you don’t have access to local GPUs or cloud credits, Hugging Face offers affordable rental options for training — and everything runs seamlessly with their model hub.


My Vision AI Model: A Quick Overview

I fine-tuned a ResNet-based convolutional neural network using a curated dataset of labeled X-ray images, focusing on binary classification: pneumonia vs. normal. Here's what the training process looked like:

  • Model Architecture: ResNet (CNN-based)

  • Dataset: Large collection of chest X-rays

  • Loss Function: CrossEntropy with label smoothing

  • Evaluation: Accuracy, precision, recall

  • Best Accuracy: 95% on validation set

Thanks to Hugging Face’s tooling and GPU support, I was able to monitor training live, test variations quickly, and tune hyperparameters more effectively than traditional on-prem setups.


What’s Next?

Now that the model is live, I’m looking into clinical testing, integration into radiology workflow tools, and extending the model to detect other abnormalities such as tuberculosis or collapsed lungs.

Feel free to explore the model and training logs, and let me know what you think. I hope this encourages more developers and researchers to jump into medical AI — especially using open platforms like Hugging Face!


AI Is Just a Tool—Use It or Lose Out

I often tell people: AI is like a power tool in a carpenter’s toolbox. Give it to a skilled worker, and they’ll build something amazing. Give it to someone untrained, and it might gather dust—or worse, cause harm.

AI isn’t here to replace human oversight—at least, not yet. But let’s be real: those who refuse to use AI might soon be replaced by those who do.

So if you’ve ever been curious about building an AI model or want to explore machine learning in healthcare or any other industry, Hugging Face is a great place to start. Don’t wait. The tools are here—how you use them is up to you.


Got questions or want to collaborate? Drop me a message — I’m always happy to share notes or work on new ideas.

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