Update README.md

README.md

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-# Real‑Life Industrial Dataset of Casting Product 🏭
+# Real‑Life Industrial Dataset of Casting Product
 
-A real‑world industrial image dataset for detecting defects in casting products via quality inspection workflows.
+Disesuaikan kembali oleh tim 200 OK untuk keperluan membuat model prediksi menggunakan Xception untuk mendeteksi cacat pada _impeller submersible pump_.
 
----
+## Tentang Dataset
+Dimensi: 512 x 512 px _grayscale_<br>
+Dataset ini tanpa augmentasi lebih lanjut<br>
+Total gambar: 1300 gambar<br>
+* 519 gambar berlabel ok_front
+* 781 gambar berlabel def_front
 
-## 📦 Dataset Overview
+## Penggunaan Dataset
+Untuk _training_: 1040 gambar<br>
+Untuk _validation_: 260 gambar<br>
 
-* **Type**: Image classification, defect detection
-* **Source**: Casting product image dataset (submersible pump impellers), top‑view photos ⚙️ ([Labellerr][1], [Medium][2], [Kaggle][3])
-* **Total images**:
-
-  * \~6,633 training images
-  * \~715 test images ([Medium][4], [Medium][2])
-* **Image size**: 300×300 px (some sources mention 512×512) grayscale ([Medium][4])
-* **Classes (binary)**:
-
-  * `ok_front` (non-defective)
-  * `def_front` (defective)
-
----
-
-## 📁 Directory Structure
-
-```
-casting_data/
-├── train/
-│   ├── ok_front/         # Non-defective images (~2,875)
-│   └── def_front/        # Defective images (~3,758)
-└── test/
-    ├── ok_front/         # Non-defective (~262)
-    └── def_front/        # Defective (~453)
-```
-
----
-
-## 🎯 Use Cases
-
-* **Automated visual quality inspection** in manufacturing
-* Binary classification (defective vs. non‑defective)
-* Convolutional Neural Network (CNN) model training & evaluation
-* Transfer learning or fine-tuning on use-specific defects
-
----
-
-## 🧪 Example Model Performance
-
-* CNNs reach up to **99%+ accuracy** in defect classification ([Labellerr][1], [Medium][2], [Google Sites][5])
-* Logistic regression on raw images performs much worse (\~73% accuracy) ([Google Sites][5])
-
-These results show deep learning is well-suited for this kind of QC task.
-
----
-
-## 🛠️ Setup & Usage
-
-### 1. Install dependencies
-
-```bash
-pip install tensorflow keras numpy matplotlib
-```
-
-### 2. Prepare data
-
-Download and extract the dataset into your working directory:
-
-```
-casting_data/
-├── train/
-│   └── ...
-└── test/
-    └── ...
-```
-
-### 3. Load data with `ImageDataGenerator`
-
-Example for training and testing pipelines:
-
-```python
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-
-# Training generator
-train_gen = ImageDataGenerator(rescale=1./255).flow_from_directory(
-    'casting_data/train',
-    target_size=(300,300),
-    color_mode='grayscale',
-    class_mode='binary',
-    batch_size=32
-)
-
-# Testing generator
-test_gen = ImageDataGenerator(rescale=1./255).flow_from_directory(
-    'casting_data/test',
-    target_size=(300,300),
-    color_mode='grayscale',
-    class_mode='binary',
-    batch_size=32,
-    shuffle=False
-)
-```
-
-### 4. Define & Train CNN
-
-Simple Keras example:
-
-```python
-from tensorflow.keras import models, layers
-
-model = models.Sequential([
-  layers.Conv2D(32, (3,3), activation="relu", input_shape=(300,300,1)),
-  layers.MaxPooling2D(2),
-  layers.Conv2D(64, (3,3), activation="relu"),
-  layers.MaxPooling2D(2),
-  layers.Flatten(),
-  layers.Dense(128, activation="relu"),
-  layers.Dense(1, activation="sigmoid")
-])
-
-model.compile(loss="binary_crossentropy", optimizer="adam",
-              metrics=["accuracy"])
-
-model.fit(train_gen, epochs=10, validation_data=test_gen)
-```
-
-### 5. Evaluate & Predict
-
-```python
-loss, acc = model.evaluate(test_gen)
-print(f"Test accuracy: {acc:.2%}")
-```
-
----
-
-## 🔍 References & Further Reading
-
-* Medium guide on casting defect classification and CNN performance ([Medium][4], [Labellerr][1], [Analytics Journal][6])
-* Analytics Vidhya case study splitting \~3,758 defective and \~2,875 non‑defective images in training, and \~453/262 in test sets; all grayscale with size 300×300 px ([Medium][4])
-* Labellerr blog on automated casting inspection with CNNs (7,300 images, similar structure) ([Labellerr][1])
+## Sumber Dataset
+Klik tautan [ini](https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product) untuk melihat _dataset_ asli.