Update README.md

README.md

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 ---
 license: apache-2.0
+library_name: keras
+pipeline_tag: image-classification
+tags:
+- keras
+- tensorflow
+- efficientnet
+- efficientnet-b3
+- image-classification
+- skin-disease
+- medical-ai
 ---
+
+# EfficientNet-B3 Skin Disease Classifier
+
+## Overview
+
+This repository provides an EfficientNet-B3 image classification model trained to recognize common skin diseases from clinical skin images.
+
+The model is intended for research and educational purposes only and should not be used as a substitute for professional medical diagnosis.
+
+---
+
+## Model Architecture
+
+- Backbone: EfficientNet-B3
+- Framework: TensorFlow / Keras
+- Task: Multi-class Image Classification
+
+---
+
+## Disease Classes
+
+The model predicts one of the following classes:
+
+- Allergic Contact Dermatitis
+- Eczema
+- Folliculitis
+- Insect Bite
+- Irritant Contact Dermatitis
+- Psoriasis
+- Tinea
+- Urticaria
+
+---
+
+## Dataset
+
+The model was trained on an 8-class skin disease dataset derived from the SCIN dataset with additional preprocessing and data balancing.
+
+---
+
+## Input
+
+- RGB Image
+- Image Size: 300 × 300 pixels
+
+---
+
+## Output
+
+The model returns the probability for each disease class.
+
+Example:
+
+| Disease   | Probability |
+|-----------|-------------|
+| Eczema    | 0.82        |
+| Psoriasis | 0.10        |
+| Tinea     | 0.04        |
+
+---
+
+## Files
+
+| File                        | Description                  |
+|-----------------------------|------------------------------|
+| efficientnet_b3.keras       | Trained classification model |
+| efficientnet_backbone.keras | EfficientNet backbone        |
+| label_mapping.json          | Class index mapping          |
+| training_config.json        | Training configuration       |
+
+---
+
+## Example
+
+```python
+import tensorflow as tf
+
+model = tf.keras.models.load_model("efficientnet_b3.keras")
+```
+
+---
+
+## Intended Use
+
+This model is designed for:
+
+- Academic research
+- Computer Vision experiments
+- Medical AI education
+- Prototype applications
+
+---
+
+## Limitations
+
+- Not intended for clinical diagnosis.
+- Performance depends on image quality.
+- Predictions should always be interpreted by healthcare professionals.
+
+---
+
+## Author
+
+**Chantaro Ntw**
+
+AI Engineer | Computer Vision | Medical AI