---
license: mit
tags:
  - image-classification
  - resnet50
  - medical
  - acne-detection
task:
  - image-classification
output:
  - label: "level1"
    score: 0.98
widget:
  - text: "example_image.jpg"
    output:
      - label: "level3"
        score: 0.85
---

# ResNet-50 Model for Acne Severity Classification

This is a fine-tuned ResNet-50 model designed to classify the severity of acne from medical images into five categories (Severity 1 to Severity 5). The model leverages transfer learning on ResNet-50 pre-trained on ImageNet and adapts it for acne severity classification tasks.

## Model Details

### Training Details
- **Framework:** PyTorch
- **Base Model:** ResNet-50 (pretrained on ImageNet)
- **Dataset:** A balanced dataset of acne images annotated with severity levels (Severity 1 to 5).
- **Preprocessing:** Images resized to 224x224 pixels, normalized using ImageNet statistics (mean: `[0.485, 0.456, 0.406]`, std: `[0.229, 0.224, 0.225]`).
- **Optimizer:** Adam with a learning rate of 0.001.
- **Loss Function:** CrossEntropyLoss.
- **Epochs:** 10.
- **Validation Accuracy:** 0.85 (on a held-out validation set).

## Intended Use

This model is intended for educational purposes and demonstrates image classification for medical images. It should not be used for clinical decision-making without further validation.

## Example Usage

You can use this model via the Hugging Face Transformers pipeline for inference. Ensure you have the `transformers` library installed:

```bash
pip install transformers