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import requests
import zipfile
import io
import os
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model
from sklearn.utils.class_weight import compute_class_weight
from keras.layers import Dense, GlobalAveragePooling2D
from keras.layers import Dropout
output_dir = "./data"
url = "https://huggingface.co/datasets/garythung/trashnet/resolve/main/dataset-resized.zip"
# Mendownload file ZIP (mungkin bisa dihindari jika sudah tersedia secara lokal)
response = requests.get(url)
with zipfile.ZipFile(io.BytesIO(response.content)) as zip_ref:
zip_ref.extractall(output_dir)
data_dir = './data/dataset-resized'
garbage_types = os.listdir(data_dir)
# Menggunakan pandas untuk memanipulasi file path gambar
data = []
for garbage_type in garbage_types:
garbage_type_path = os.path.join(data_dir, garbage_type)
if os.path.isdir(garbage_type_path):
for file in os.listdir(garbage_type_path):
data.append((os.path.join(garbage_type_path, file), garbage_type))
df = pd.DataFrame(data, columns=['filepath', 'label'])
# Split dataset
train_df, val_df = train_test_split(df, test_size=0.2, random_state=42, stratify=df['label'])
# Augmentasi dan generator data
train_datagen = ImageDataGenerator(
rotation_range=60,
width_shift_range=0.15,
height_shift_range=0.15,
zoom_range=0.20,
horizontal_flip=True,
vertical_flip=True,
shear_range=0.05,
brightness_range=[0.9, 1.1],
channel_shift_range=10,
fill_mode='nearest',
preprocessing_function=preprocess_input
)
val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
train_generator = train_datagen.flow_from_dataframe(
dataframe=train_df,
x_col="filepath",
y_col="label",
target_size=(384, 384),
batch_size=32,
class_mode='categorical',
shuffle=False
)
val_generator = val_datagen.flow_from_dataframe(
dataframe=val_df,
x_col="filepath",
y_col="label",
target_size=(384, 384),
batch_size=32,
class_mode='categorical',
shuffle=False
)
class_labels = train_df['label'].unique()
class_labels
train_generator.class_indices
weights = compute_class_weight(class_weight='balanced', classes=class_labels, y=train_df['label'])
class_weights = dict(zip(train_generator.class_indices.values(), weights))
# Model
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(384, 384, 3))
for layer in base_model.layers[:143]:
layer.trainable = False
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.5)(x)
x = Dense(6, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=x)
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
# Callbacks
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, min_lr=0.00001)
early_stopping = EarlyStopping(monitor='val_loss', mode='min', patience=8, restore_best_weights=True, verbose=1)
model_checkpoint = ModelCheckpoint(filepath="best_model.keras", monitor="val_loss", save_best_only=True, verbose=1)
callbacks = [reduce_lr, early_stopping, model_checkpoint]
# Model training
history = model.fit(
train_generator,
epochs=50,
validation_data=val_generator,
class_weight=class_weights,
callbacks=callbacks
)
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