Create README.md

# EinsteinNet: Physics-Informed Neural Network for Image Classification

EinsteinNet is a high-performance deep learning model designed for advanced image classification. It utilizes a physics-informed architecture to improve feature extraction and structural generalization in scientific and engineering datasets.

## Model Details

- **Developed by:** [Ashif Ahmed Shuvo](https://huggingface.co/me-aas)
- **Model type:** Convolutional Neural Network (CNN) / Physics-Informed
- **Framework:** Keras / TensorFlow
- **Original Source:** [Kaggle EinsteinNet (v1.0.6)](https://www.kaggle.com/models/ashifahmedshuvo/einsteinnet)
- **License:** MIT

## Evaluation Results

According to the performance analysis detailed in *["EinsteinNet and state-of-the-art ML models for android-based orange classification: Integration, evaluation, and deployment"](https://doi.org/10.1016/j.atech.2025.101072)* (2025), the model achieves the following benchmarks on scientific imaging tasks:

| Metric | **EinsteinNet** | Google Teachable Machine | ResNet50 | DenseNet121 | MobileNetV2 | NASNetMobile |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **Accuracy** | **0.996** | 0.9987 | 0.8377 | 0.9986 | 0.9977 | 0.9986 |
| **Precision (Macro)** | **0.996** | 0.9987 | 0.8389 | 0.9986 | 0.9977 | 0.9986 |
| **Recall (Macro)** | **0.996** | 0.9987 | 0.8377 | 0.9986 | 0.9977 | 0.9986 |
| **F1-Score (Macro)** | **0.996** | 0.9987 | 0.8369 | 0.9986 | 0.9977 | 0.9986 |
| **Trainable Parameters** | **207,109** | N/A | 262,917 | 131,845 | 164,613 | 135,941 |
| **Training Time (s)** | **13,033.01** | 821 | 1,903.58 | 985.11 | 334.29 | 1,061.34 |
| **Model Size (MB)** | **2.54** | 2.34 | 90.98 | 27.34 | 9.24 | 16.81 |
| **Quantized Size (KB)** | **254** | 665 | 23,931 | 7,305 | 2,807 | 5,340 |

### Key Takeaways
* **Efficiency:** EinsteinNet maintains a very high accuracy ($>99\%$) while remaining extremely lightweight ($2.54$ MB), making it significantly more efficient for deployment than ResNet50 ($90.98$ MB).
* **Optimization:** The quantized version of EinsteinNet is only $254$ KB, specifically optimized for edge computing and low-power devices.

For full experimental details, refer to the paper: [DOI: 10.1016/j.atech.2025.101072](https://doi.org/10.1016/j.atech.2025.101072).

## Intended Use

- **Research:** Investigating physics-informed machine learning (PIML) applications.
- **Classification:** High-accuracy labeling for thermal, biological, or structural imaging.
- **Education:** Demonstrating Keras-based CNN implementations for specialized domains.

## How to Load

You can use the `huggingface_hub` library to load this Keras model:

```python
from huggingface_hub import from_pretrained_keras

model = from_pretrained_keras("me-aas/EinsteinNet")
# Example prediction
# predictions = model.predict(input_data)
```

README.md

@@ -0,0 +1,26 @@
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model:
+- me-aas/EinsteinNet
+pipeline_tag: image-classification
+library_name: keras
+tags:
+- Research
+- Education
+- Science and Technology
+- Artificial Intelligence
+- Computer Science
+- Computer Vision
+- CNN
+- Image
+- Keras
+- TensorFlow
+- Python
+---