update readme

README.md

@@ -21,8 +21,20 @@ widget:
 
 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 Overview
+
 ## Model Details
 
+### Key Features
+- **Input:** Medical images of acne-affected skin.
+- **Output:** Severity classification with one of the following labels:
+  - `level0` (No acne or minimal severity)
+  - `level1` (Mild severity)
+  - `level2` (Moderate severity)
+  - `level3` (Severe or advanced acne)
+
 ### Training Details
 - **Framework:** PyTorch
 - **Base Model:** ResNet-50 (pretrained on ImageNet)
@@ -33,6 +45,10 @@ This is a fine-tuned ResNet-50 model designed to classify the severity of acne f
 - **Epochs:** 10.
 - **Validation Accuracy:** 0.85 (on a held-out validation set).
 
+---
+
+## How to Use the Model
+
 ## 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.
@@ -42,4 +58,15 @@ This model is intended for educational purposes and demonstrates image classific
 You can use this model via the Hugging Face Transformers pipeline for inference. Ensure you have the `transformers` library installed:
 
 ```bash
-pip install transformers
+pip install transformers
+```
+
+### Hugging Face Inference API
+
+You can use the model via the Hugging Face Inference API by sending an image encoded in base64. Here’s an example:
+
+```bash
+curl -X POST https://api-inference.huggingface.co/models/YOUR_MODEL_NAME \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"inputs": "BASE64_ENCODED_IMAGE"}'