---
license: mit
tags:
  - skin-disease
  - medical
  - image-classification
  - keras
  - efficientnet
  - dermatology
  - computer-vision
  - healthcare
datasets:
  - custom
widget:
  - src: https://huggingface.co/spaces/Tanishq77/skin-condition-classifier
    example_title: Try it live with Streamlit!
---
# 🧠 Skin Condition Classifier (EfficientNetV2B0)

This model classifies **facial skin images** into **6 common dermatological conditions** using a fine-tuned [EfficientNetV2B0](https://keras.io/api/applications/efficientnet/#efficientnetv2b0-function) architecture.

## 📌 Supported Skin Conditions
- Acne
- Carcinoma
- Eczema
- Keratosis
- Milia
- Rosacea

## 🧪 Model Performance
- **Final Test Accuracy**: `95.60%`
- Evaluated on a balanced, augmented custom dataset of real-world dermatological images.
- **Confusion Matrix** indicates strong separation for all classes, with minor overlap in visually similar conditions.

## 🏗️ Model Architecture

- ✅ **Backbone**: EfficientNetV2B0 (pretrained on ImageNet)
- 🔁 Global Average Pooling + Dense(512, ReLU) + Dropout(0.4) + Dense(6, Softmax)
- 📉 **Loss Function**: `sparse_categorical_crossentropy`
- ⚖️ **Class Weights**: Applied to handle minor variations
- 🧠 Fine-tuned with learning rate scheduling and layer unfreezing

## 🧠 Usage

### 🔍 Load the model (Keras format)

```python
from tensorflow.keras.models import load_model
model = load_model("path/to/saved_skin_model")
````

### 🖼️ Input Format

* Image size: **224x224**
* Input dtype: `float32`
* Preprocessing: Use `preprocess_input` from `tensorflow.keras.applications.efficientnet_v2`

```python
from tensorflow.keras.applications.efficientnet_v2 import preprocess_input
import numpy as np
img = preprocess_input(img)  # Ensure shape is (1, 224, 224, 3)
pred = model.predict(img)
```

### 📊 Output Format

* Softmax probabilities for 6 classes.
* `argmax(pred)` gives class index from 0–5.

---

## 🧾 License

[MIT License](LICENSE)

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## 👨‍💻 Author

**Tanishq Shinde**
B.E. Computer Engineering, PICT
🔗 [GitHub](https://github.com/Tanishq-789) | [LinkedIn](https://linkedin.com/in/tanishqshinde) | [Hugging Face](https://huggingface.co/Tanishq77)

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> 🔬 For educational and non-diagnostic purposes only. Always consult a medical professional.

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---