metadata

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:

pip install transformers