Kaggle Face Mask Types Dataset
사용 라이브러리
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import glob
import pathlib
import splitfolders
import PIL
import cv2
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers
from tensorflow.keras import Sequential
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras.utils import plot_model
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
from sklearn.metrics import classification_report
Data Load
1
2
3
total_img = glob.glob("Dataset/train/*/*")
print(f"Total Images: {len(total_img)}")
1
Total Images: 1956
1
2
3
4
5
6
total_img = pd.Series(total_img)
total_df = pd.DataFrame()
total_df["Filename"] = total_img.map(lambda img_name: img_name.split("\\")[-1])
total_df["ClassId"] = total_img.map(lambda img_name: img_name.split("\\")[-2])
total_df.head()
| Filename | ClassId | |
|---|---|---|
| 0 | 0.jpg | cloth |
| 1 | 1.jpg | cloth |
| 2 | 10.jpg | cloth |
| 3 | 100.jpg | cloth |
| 4 | 101.jpg | cloth |
1
2
class_id_distributionTotal = total_df["ClassId"].value_counts()
class_id_distributionTotal
1
2
3
4
5
6
nfm 474
cloth 396
n95v 390
n95 354
srg 342
Name: ClassId, dtype: int64
1
2
plt.figure(facecolor="white")
_ = plt.pie(class_id_distributionTotal.values, labels=class_id_distributionTotal.index, autopct="%.2f%%")
Data Split
1
2
# Train : Test : Val = 80 : 10 : 10
splitfolders.ratio(input="Dataset/train/", output="output", ratio=(0.8, 0.1, 0.1))
1
Copying files: 1956 files [00:01, 1260.90 files/s]
1
2
3
4
5
6
7
8
9
train_img = glob.glob("./output/train/*/*")
test_img = glob.glob("./output/test/*/*")
val_img = glob.glob("./output/val/*/*")
print(f"\
training images: {len(train_img)}\n\
validation images: {len(val_img)}\n\
test images: {len(test_img)}\
")
1
2
3
training images: 1563
validation images: 194
test images: 199
1
2
3
4
5
6
7
data = [len(train_img), len(val_img), len(test_img)]
labels = ["Train", "Val", "Test"]
plt.figure(facecolor="white")
_ = plt.pie(data, labels=labels, autopct="%.2f%%")
plt.title("Ratio - [Train : Val : Test]")
plt.show()
Train / Validation / Test
1
2
3
4
5
6
7
8
9
10
11
12
# Train
train_image_names = pd.Series(train_img)
train_df = pd.DataFrame({"Filename":[x for x in train_image_names.map(lambda img_name: img_name.split("\\")[-1])],
"ClassId":[x for x in train_image_names.map(lambda img_name: img_name.split("\\")[-2])]})
# Val
val_image_names = pd.Series(val_img)
val_df = pd.DataFrame({"Filename":[x for x in val_image_names.map(lambda img_name: img_name.split("\\")[-1])],
"ClassId":[x for x in val_image_names.map(lambda img_name: img_name.split("\\")[-2])]})
# Test
test_image_names = pd.Series(test_img)
test_df = pd.DataFrame({"Filename":[x for x in test_image_names.map(lambda img_name: img_name.split("\\")[-1])],
"ClassId":[x for x in test_image_names.map(lambda img_name: img_name.split("\\")[-2])]})
1
2
3
4
5
6
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
for idx, df in enumerate([train_df, val_df, test_df]):
axex = ax[idx]
df = df["ClassId"].value_counts()
axex.pie(df.values, labels=df.index, autopct="%.2f%%")
plt.show()
Show Image
1
2
3
4
5
6
7
8
9
plot_df = train_df.sample(12).reset_index()
plt.figure(figsize=(15, 15))
for _ in range(12):
img_name = plot_df.loc[_, "Filename"]
label_str = plot_df.loc[_, "ClassId"]
plt.subplot(4, 4, _+1)
plt.imshow(plt.imread(os.path.join("./output/train/", label_str, img_name)))
plt.title(label_str)
Image Data Generator
1
2
3
4
5
6
7
train_datagen = ImageDataGenerator(zoom_range=0.15, width_shift_range=0.2, shear_range=0.15)
test_datagen = ImageDataGenerator()
val_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory("./output/train/", target_size=(300, 300), batch_size=32, shuffle=True, class_mode='categorical')
test_generator = test_datagen.flow_from_directory("./output/test/", target_size=(300,300), batch_size=32, shuffle=False, class_mode='categorical')
val_generator = val_datagen.flow_from_directory("./output/val/", target_size=(300,300), batch_size=32, shuffle=False, class_mode='categorical')
1
2
3
Found 1563 images belonging to 5 classes.
Found 199 images belonging to 5 classes.
Found 194 images belonging to 5 classes.
Modeling - Efficent Net
1
2
3
4
5
6
7
from tensorflow.keras.applications import EfficientNetB0
model = EfficientNetB0(
input_shape=(300, 300, 3),
include_top=False,
weights="imagenet"
)
Fine Tuning
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
model.trainable = True
for layer in model.layers[:-15]:
layer.trainable = False
x = tf.keras.layers.Flatten()(model.output)
x = tf.keras.layers.Dropout(0.5)(x)
x = tf.keras.layers.Dense(5, activation="softmax")(x)
model = tf.keras.Model(model.input, x)
model.compile(
optimizer = "adam",
loss = tf.keras.losses.CategoricalCrossentropy(),
metrics = ["accuracy"]
)
Train
1
2
early_stopping = EarlyStopping(monitor="val_accuracy", mode="max", verbose=0, patience=20)
# model_check = ModelCheckpoint("model.h5", monitor="val_accuracy", mode="max", save_best_only=True)
1
2
with tf.device("/device:GPU:0"):
history = model.fit(train_generator, validation_data=val_generator, epochs=50, verbose=1, callbacks=[early_stopping])
1
2
3
4
5
6
7
8
9
10
11
Epoch 1/50
49/49 [==============================] - 25s 517ms/step - loss: 0.3727 - accuracy: 0.9469 - val_loss: 0.9907 - val_accuracy: 0.8918
Epoch 2/50
49/49 [==============================] - 26s 521ms/step - loss: 0.2210 - accuracy: 0.9623 - val_loss: 0.9097 - val_accuracy: 0.9021
Epoch 3/50
49/49 [==============================] - 26s 522ms/step - loss: 0.3565 - accuracy: 0.9584 - val_loss: 0.9695 - val_accuracy: 0.9175
...
Epoch 47/50
49/49 [==============================] - 31s 623ms/step - loss: 0.4125 - accuracy: 0.9834 - val_loss: 1.8275 - val_accuracy: 0.9227
Epoch 48/50
49/49 [==============================] - 27s 553ms/step - loss: 0.2847 - accuracy: 0.9795 - val_loss: 2.2491 - val_accuracy: 0.9124
1
2
3
4
5
6
hist_df = pd.DataFrame(history.history)
fig, ax = plt.subplots(1, 2, figsize=(14, 5))
hist_df[["accuracy", "val_accuracy"]].plot(ax=ax[0])
hist_df[["loss", "val_loss"]].plot(ax=ax[1])
plt.show()
Model Evaliation
1
2
3
test_loss, test_acc = model.evaluate(test_generator, steps=len(test_generator), verbose=1)
print('Loss: %.3f' % (test_loss * 100.0))
print('Accuracy: %.3f' % (test_acc * 100.0))
1
2
3
7/7 [==============================] - 3s 332ms/step - loss: 4.2478 - accuracy: 0.8995
Loss: 424.777
Accuracy: 89.950
1
2
3
4
5
y_val = test_generator.classes
y_pred = model.predict(test_generator)
y_pred = np.argmax(y_pred,axis=1)
print(classification_report(y_val, y_pred))
1
2
3
4
5
6
7
8
9
10
11
precision recall f1-score support
0 0.88 0.85 0.86 41
1 0.91 0.81 0.85 36
2 0.86 0.95 0.90 39
3 0.96 1.00 0.98 48
4 0.88 0.86 0.87 35
accuracy 0.90 199
macro avg 0.90 0.89 0.89 199
weighted avg 0.90 0.90 0.90 199
Comments powered by Disqus.