Update README.md

README.md

@@ -3,4 +3,119 @@ license: mit
 base_model:
 - google/efficientnet-b1
 pipeline_tag: image-classification
----
+---
+
+# 🖼️ RateBooru Efficient - Danbooru Rating Classifier
+
+This is a simple image classification model fine-tuned from **EfficientNetB1** to classify Danbooru-style image ratings into 3 categories:
+
+- `general`
+- `questionable`
+- `explicit`
+
+## 🎯 Purpose
+
+This model was created as a **learning project** and also intended for **basic NSFW moderation tasks** based on Danbooru rating style.
+
+## 📊 Dataset
+
+- Total images: **6,000**
+  - Training: 4,800
+  - Validation: 1,200
+- All images are tagged with **rating metadata** from Danbooru.
+- The dataset is private / custom-collected, not provided here.
+
+## 🧠 Model Details
+
+- Base model: `EfficientNetB1` from TensorFlow/Keras
+- Fine-tuned for 50 epochs
+- Input size: 240x240 RGB
+- Output: Softmax classification (3 classes)
+
+## 📈 Final Training Metrics
+
+| Metric        | Training | Validation |
+|---------------|----------|------------|
+| Accuracy      | 75.24%   | 70.42%     |
+| Loss          | 0.5916   | 0.6452     |
+| Precision     | 79.07%   | 72.93%     |
+| Recall        | 69.09%   | 67.58%     |
+| Learning Rate | 2e-6     | -          |
+
+## 🚀 Usage (TensorFlow)
+
+```python
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+
+# === Configuration ===
+MODEL_PATH = 'ratebooru_efficientnetb1.keras'
+IMAGE_PATH = 'example.jpg'  # Change this to your image file path
+IMG_SIZE = (240, 240)
+CLASS_NAMES = ['explicit', 'general', 'questionable']
+
+# === Load & Preprocess Image ===
+def preprocess_image(image_path):
+    try:
+        img = Image.open(image_path).convert('RGB')
+        img = img.resize(IMG_SIZE)
+        img_array = tf.keras.utils.img_to_array(img)
+        return tf.expand_dims(img_array, 0)
+    except Exception as e:
+        print(f"Error loading image: {e}")
+        return None
+
+# === Predict ===
+def predict(model, image_tensor):
+    predictions = model.predict(image_tensor)
+    score = tf.nn.softmax(predictions[0])
+    return {CLASS_NAMES[i]: float(score[i]) for i in range(len(CLASS_NAMES))}
+
+# === Main ===
+if not os.path.exists(MODEL_PATH):
+    print(f"Model not found at '{MODEL_PATH}'")
+    exit()
+
+if not os.path.exists(IMAGE_PATH):
+    print(f"Image not found at '{IMAGE_PATH}'")
+    exit()
+
+print("Loading model...")
+model = tf.keras.models.load_model(MODEL_PATH)
+print("Model loaded.")
+
+print(f"Predicting image: {IMAGE_PATH}")
+image_tensor = preprocess_image(IMAGE_PATH)
+if image_tensor is None:
+    exit()
+
+results = predict(model, image_tensor)
+
+print("\\n--- Prediction Result ---")
+for class_name, confidence in sorted(results.items(), key=lambda x: x[1], reverse=True):
+    print(f"{class_name:<15}: {confidence * 100:.2f}%")
+```
+
+```bash
+Loading model...
+Model loaded.
+Predicting image: example.png
+
+--- Prediction Result ---
+general        : 54.99%
+questionable   : 22.57%
+explicit       : 22.44%
+
+```
+
+## ⚠️ Disclaimer
+
+- This model is not perfect and might misclassify borderline content.
+- **Do not** use it for serious moderation or legal filtering without thorough evaluation.
+- Dataset is based on subjective human tagging (Danbooru), which may include biases.
+
+## 📄 License
+
+MIT License