Kaggle : Paddy Disease Classification
Paddy Disease Classification
https://www.kaggle.com/competitions/paddy-disease-classification/overview
- 쌀의 잎 모양을 보고 현재 질병을 있는 없는지, 질병이 있으면 어떤 질병인지 판단하는 Competition입니다.
- 기본적으로 Image Classification으로 진행해 보도록 하겠습니다.
0. Train
import pandas as pd
import os
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GlobalAveragePooling2D , BatchNormalization , Dropout , Dense
from tensorflow.keras.callbacks import TensorBoard , ModelCheckpoint , LearningRateScheduler , EarlyStopping
tf.__version__
'2.10.0-dev20220727'
BATCH_SIZE = 64
DROP_OUT_RATE = 0.2
train_csv = pd.read_csv("./paddy-disease-classification/train.csv")
train_csv.head()
| image_id | label | variety | age | |
|---|---|---|---|---|
| 0 | 100330.jpg | bacterial_leaf_blight | ADT45 | 45 |
| 1 | 100365.jpg | bacterial_leaf_blight | ADT45 | 45 |
| 2 | 100382.jpg | bacterial_leaf_blight | ADT45 | 45 |
| 3 | 100632.jpg | bacterial_leaf_blight | ADT45 | 45 |
| 4 | 101918.jpg | bacterial_leaf_blight | ADT45 | 45 |
train_csv['label'].value_counts()
normal 1764
blast 1738
hispa 1594
dead_heart 1442
tungro 1088
brown_spot 965
downy_mildew 620
bacterial_leaf_blight 479
bacterial_leaf_streak 380
bacterial_panicle_blight 337
Name: label, dtype: int64
train_csv['variety'].value_counts()
ADT45 6992
KarnatakaPonni 988
Ponni 657
AtchayaPonni 461
Zonal 399
AndraPonni 377
Onthanel 351
IR20 114
RR 36
Surya 32
Name: variety, dtype: int64
- 필요한 것은 ‘image_id’와 ‘label’뿐입니다.
image_id = train_csv['image_id'].tolist()
label = train_csv['label'].tolist()
train_csv['file_path'] = "./paddy-disease-classification/train_images/" + train_csv['label'] + "/" + train_csv['image_id']
train_csv
| image_id | label | variety | age | file_path | |
|---|---|---|---|---|---|
| 0 | 100330.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| 1 | 100365.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| 2 | 100382.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| 3 | 100632.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| 4 | 101918.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| ... | ... | ... | ... | ... | ... |
| 10402 | 107607.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
| 10403 | 107811.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
| 10404 | 108547.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
| 10405 | 110245.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
| 10406 | 110381.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
10407 rows × 5 columns
file_path = train_csv['file_path'].tolist()
file_path
['./paddy-disease-classification/train_images/bacterial_leaf_blight/100330.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/100365.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/100382.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/100632.jpg',
'./paddy-disease-classification/train_images/bacterial_panicle_blight/104162.jpg',
'./paddy-disease-classification/train_images/bacterial_panicle_blight/104300.jpg',
'./paddy-disease-classification/train_images/bacterial_panicle_blight/104460.jpg',
'./paddy-disease-classification/train_images/bacterial_panicle_blight/104684.jpg',
'./paddy-disease-classification/train_images/bacterial_panicle_blight/104815.jpg',
'./paddy-disease-classification/train_images/bacterial_panicle_blight/104853.jpg',
'./paddy-disease-classification/train_images/bacterial_panicle_blight/104912.jpg',
'./paddy-disease-classification/train_images/bacterial_panicle_blight/105065.jpg',
...]
- Target은 정상상태(normal)을 포함하여 총 10가지 입니다.
le = LabelEncoder()
le.fit(label)
print(le.classes_)
['bacterial_leaf_blight' 'bacterial_leaf_streak'
'bacterial_panicle_blight' 'blast' 'brown_spot' 'dead_heart'
'downy_mildew' 'hispa' 'normal' 'tungro']
- 총 5개의 Train / Val. Set으로 나누어 5개의 Model을 만든 후, 가장 성능이 좋은 하나의 Model로 Predict합니다.
from sklearn.model_selection import StratifiedKFold
skf = StratifiedKFold(n_splits=5,random_state=27, shuffle=True)
skf
StratifiedKFold(n_splits=5, random_state=27, shuffle=True)
folds = []
for train_index, val_index in skf.split(file_path, label):
X_train = [file_path[i] for i in train_index ]
X_val = [file_path[i] for i in val_index ]
label_train= [label[i] for i in train_index ]
label_val = [label[i] for i in val_index ]
le_label = le.transform(label_train)
label_train = tf.keras.utils.to_categorical(le_label , num_classes=10)
le_label = le.transform(label_val)
label_val = tf.keras.utils.to_categorical(le_label , num_classes=10)
folds.append([X_train,label_train,X_val,label_val])
len(folds)
5
len(folds[0][0]) , len(folds[0][1]) , len(folds[0][2]) , len(folds[0][3])
(8325, 8325, 2082, 2082)
folds[0][1][0]
array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)
- Data Augmentation을 Train Dataset에만 적용합니다.
def load_image_train( image_path , label):
img = tf.io.read_file(image_path)
img = tf.image.decode_png(img, channels=3)
# Train Dataset에만 Augmentation을 적용
img = tf.image.random_flip_left_right(img)
img = tf.image.random_flip_up_down(img)
img = tf.image.random_crop(img , tf.shape(img))
img = tf.image.random_contrast(img , 0.5 , 1.5)
img = tf.image.random_saturation(img, 0.75, 1.25)
img = tf.image.random_brightness(img, 0.2)
img = tf.image.resize(img, (224, 224))
img = tf.keras.applications.efficientnet_v2.preprocess_input(img)
return img , label
def load_image_val( image_path , label):
img = tf.io.read_file(image_path)
img = tf.image.decode_png(img, channels=3)
img = tf.image.resize(img, (224, 224))
img = tf.keras.applications.efficientnet_v2.preprocess_input(img)
return img , label
- Callback들을 정의합니다.
initial_learning_rate = 0.01
def lr_exp_decay(epoch, lr):
k = 0.1
return initial_learning_rate * np.math.exp(-k*epoch)
lr_scheduler = LearningRateScheduler(lr_exp_decay, verbose=1)
log_dir = os.path.join('Logs')
tb_callback = TensorBoard(log_dir=log_dir)
es = EarlyStopping(monitor = 'val_accuracy', patience = 3, mode = 'auto')
-
EfficientNet으로 Feature Extraction합니다.
-
각 Fold 마다 다른 Model을 Train합니다.
def generate_model():
effnet_v2 = tf.keras.applications.efficientnet_v2.EfficientNetV2B0(
weights=None,
include_top=False)
model= Sequential()
model.add( effnet_v2 )
model.add( GlobalAveragePooling2D() )
model.add( Dropout( DROP_OUT_RATE ) )
model.add( BatchNormalization() )
model.add( Dense(128, activation='relu') )
model.add( Dropout( DROP_OUT_RATE ) )
model.add( BatchNormalization() )
model.add( Dense(10, activation='softmax') )
model.compile(
optimizer=tf.keras.optimizers.Adam(1e-3),
loss='categorical_crossentropy',
metrics=['accuracy']
)
return model
- 각 Fold별로 Train 시작합니다.
for idx,fold in enumerate(folds):
# Fold 별로 Checkpoint 다르게 설정
CHECKPOINT_PATH = os.path.join('CheckPoints_' + str(idx))
cp = ModelCheckpoint(filepath=CHECKPOINT_PATH,
monitor='val_accuracy',
save_best_only = True,
verbose = 1)
train_dataset = tf.data.Dataset.from_tensor_slices( (fold[0] ,
fold[1] )
)
val_dataset = tf.data.Dataset.from_tensor_slices( (fold[2] ,
fold[3] )
)
train_dataset = train_dataset.shuffle(buffer_size=len(fold[0]))\
.map( load_image_train, num_parallel_calls=tf.data.experimental.AUTOTUNE)\
.repeat()\
.batch(BATCH_SIZE)\
.prefetch(tf.data.experimental.AUTOTUNE)
val_dataset = val_dataset.shuffle(buffer_size=len(fold[2]))\
.map( load_image_val, num_parallel_calls=tf.data.experimental.AUTOTUNE)\
.repeat()\
.batch(BATCH_SIZE)\
.prefetch(tf.data.experimental.AUTOTUNE) #
model = generate_model()
hist = model.fit(train_dataset,
validation_data=val_dataset,
callbacks=[lr_scheduler , cp , tb_callback, es],
steps_per_epoch = 2* (len(fold[0]) / BATCH_SIZE),
validation_steps = 2* (len(fold[2]) / BATCH_SIZE),
epochs = 100,
verbose = 1
)
Epoch 1: LearningRateScheduler setting learning rate to 0.01.
Epoch 1/100
261/260 [==============================] - ETA: 0s - loss: 2.3030 - accuracy: 0.1603
Epoch 1: val_accuracy improved from -inf to 0.16809, saving model to CheckPoints_0
WARNING:absl:Function `_wrapped_model` contains input name(s) efficientnetv2-b0_input with unsupported characters which will be renamed to efficientnetv2_b0_input in the SavedModel.
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 91). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: CheckPoints_0\assets
260/260 [==============================] - 149s 573ms/step - loss: 0.2825 - accuracy: 0.9074 - val_loss: 0.3361 - val_accuracy: 0.9067 - lr: 0.0017
Epoch 20: LearningRateScheduler setting learning rate to 0.0014956861922263505.
Epoch 20/100
261/260 [==============================] - ETA: 0s - loss: 0.2324 - accuracy: 0.9221
Epoch 20: val_accuracy did not improve from 0.90672
260/260 [==============================] - 107s 411ms/step - loss: 0.2324 - accuracy: 0.9221 - val_loss: 0.3662 - val_accuracy: 0.8980 - lr: 0.0015
Epoch 21: LearningRateScheduler setting learning rate to 0.0013533528323661271.
Epoch 21/100
261/260 [==============================] - ETA: 0s - loss: 0.2156 - accuracy: 0.9264
Epoch 21: val_accuracy did not improve from 0.90672
260/260 [==============================] - 102s 390ms/step - loss: 0.2156 - accuracy: 0.9264 - val_loss: 0.3430 - val_accuracy: 0.9058 - lr: 0.0014
Epoch 22: LearningRateScheduler setting learning rate to 0.001224564282529819.
Epoch 22/100
261/260 [==============================] - ETA: 0s - loss: 0.1887 - accuracy: 0.9379
Epoch 22: val_accuracy did not improve from 0.90672
260/260 [==============================] - 103s 395ms/step - loss: 0.1887 - accuracy: 0.9379 - val_loss: 0.3635 - val_accuracy: 0.9048 - lr: 0.0012
1. Choose Best Model
- 학습을 마친 5개의 Model 중에 성능이 가장 좋은 Model을 선택합니다.
- 전체 Train Set에서 가장 점수가 높은 Model을 선택합니다.
import pandas as pd
import os
import numpy as np
from sklearn.preprocessing import LabelEncoder
import tensorflow as tf
from keras.models import load_model, save_model
from tqdm import tqdm
import pandas as pd
import os
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GlobalAveragePooling2D , BatchNormalization , Dropout , Dense
from tensorflow.keras.callbacks import TensorBoard , ModelCheckpoint , LearningRateScheduler , EarlyStopping
BATCH_SIZE = 32
train_csv = pd.read_csv("./paddy-disease-classification/train.csv")
image_id = train_csv['image_id'].tolist()
label = train_csv['label'].tolist()
train_csv['file_path'] = "./paddy-disease-classification/train_images/" + train_csv['label'] + "/" + train_csv['image_id']
train_csv
| image_id | label | variety | age | file_path | |
|---|---|---|---|---|---|
| 0 | 100330.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| 1 | 100365.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| 2 | 100382.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| 3 | 100632.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| 4 | 101918.jpg | bacterial_leaf_blight | ADT45 | 45 | ./paddy-disease-classification/train_images/ba... |
| ... | ... | ... | ... | ... | ... |
| 10402 | 107607.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
| 10403 | 107811.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
| 10404 | 108547.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
| 10405 | 110245.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
| 10406 | 110381.jpg | tungro | Zonal | 55 | ./paddy-disease-classification/train_images/tu... |
10407 rows × 5 columns
file_path = train_csv['file_path'].tolist()
file_path[:10]
['./paddy-disease-classification/train_images/bacterial_leaf_blight/100330.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/100365.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/100382.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/100632.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/101918.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/102353.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/102848.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/103051.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/103702.jpg',
'./paddy-disease-classification/train_images/bacterial_leaf_blight/103920.jpg']
le = LabelEncoder()
le.fit(label)
print(le.classes_)
['bacterial_leaf_blight' 'bacterial_leaf_streak'
'bacterial_panicle_blight' 'blast' 'brown_spot' 'dead_heart'
'downy_mildew' 'hispa' 'normal' 'tungro']
label_train = train_csv['label'].tolist()
le_label = le.transform(label_train)
label_train = tf.keras.utils.to_categorical(le_label , num_classes=10)
label_train
array([[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 1.],
[0., 0., 0., ..., 0., 0., 1.],
[0., 0., 0., ..., 0., 0., 1.]], dtype=float32)
def load_image( image_path , label ):
img = tf.io.read_file(image_path)
img = tf.image.decode_png(img, channels=3)
img = tf.image.resize(img, (224, 224))
img = tf.keras.applications.efficientnet_v2.preprocess_input(img)
return img , label
dataset = tf.data.Dataset.from_tensor_slices( (file_path , label_train) )
eval_dataset = dataset.shuffle(buffer_size=len(file_path))\
.map( load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)\
.batch(BATCH_SIZE)\
.prefetch(tf.data.experimental.AUTOTUNE)
- 5개 Model을 각각 Load하여 전체 Train Set으로 평가합니다.
models = []
result = []
for idx in range(5):
model = load_model("CheckPoints_"+str(idx))
result.append( model.evaluate(eval_dataset) )
326/326 [==============================] - 17s 35ms/step - loss: 0.0657 - accuracy: 0.9845
326/326 [==============================] - 13s 35ms/step - loss: 0.0597 - accuracy: 0.9842
326/326 [==============================] - 12s 34ms/step - loss: 0.0532 - accuracy: 0.9866
326/326 [==============================] - 12s 34ms/step - loss: 0.1092 - accuracy: 0.9690
326/326 [==============================] - 13s 35ms/step - loss: 0.1551 - accuracy: 0.9529
result
[[0.06573611497879028, 0.9845296144485474],
[0.05972518026828766, 0.9842413663864136],
[0.05319569632411003, 0.9866436123847961],
[0.10924600809812546, 0.9689632058143616],
[0.15512056648731232, 0.9529163241386414]]
- 가장 점수가 높은 Model을 찾습니다.
r = np.array(result)
r.T[-1:]
array([[0.98452961, 0.98424137, 0.98664361, 0.96896321, 0.95291632]])
best_model_idx = np.argmax(r.T[-1:])
best_model_idx
2
- 2번 Model이 가장 성능이 좋습니다. 이 Model로 Submission Data를 만듭시다.
Inference
train_csv = pd.read_csv("./paddy-disease-classification/train.csv")
submission_csv = pd.read_csv("./paddy-disease-classification/sample_submission.csv")
label = train_csv['label'].tolist()
le = LabelEncoder()
le.fit(label)
print(le.classes_)
['bacterial_leaf_blight' 'bacterial_leaf_streak'
'bacterial_panicle_blight' 'blast' 'brown_spot' 'dead_heart'
'downy_mildew' 'hispa' 'normal' 'tungro']
le_label = le.transform(label)
labels = tf.keras.utils.to_categorical(le_label , num_classes=10)
submission_csv.head()
| image_id | label | |
|---|---|---|
| 0 | 200001.jpg | NaN |
| 1 | 200002.jpg | NaN |
| 2 | 200003.jpg | NaN |
| 3 | 200004.jpg | NaN |
| 4 | 200005.jpg | NaN |
submission_csv['file_path'] = "./paddy-disease-classification/test_images/" + submission_csv['image_id']
submission_csv
| image_id | label | file_path | |
|---|---|---|---|
| 0 | 200001.jpg | NaN | ./paddy-disease-classification/test_images/200... |
| 1 | 200002.jpg | NaN | ./paddy-disease-classification/test_images/200... |
| 2 | 200003.jpg | NaN | ./paddy-disease-classification/test_images/200... |
| 3 | 200004.jpg | NaN | ./paddy-disease-classification/test_images/200... |
| 4 | 200005.jpg | NaN | ./paddy-disease-classification/test_images/200... |
| ... | ... | ... | ... |
| 3464 | 203465.jpg | NaN | ./paddy-disease-classification/test_images/203... |
| 3465 | 203466.jpg | NaN | ./paddy-disease-classification/test_images/203... |
| 3466 | 203467.jpg | NaN | ./paddy-disease-classification/test_images/203... |
| 3467 | 203468.jpg | NaN | ./paddy-disease-classification/test_images/203... |
| 3468 | 203469.jpg | NaN | ./paddy-disease-classification/test_images/203... |
3469 rows × 3 columns
file_path = submission_csv['file_path'].tolist()
file_path[:10]
['./paddy-disease-classification/test_images/200001.jpg',
'./paddy-disease-classification/test_images/200002.jpg',
'./paddy-disease-classification/test_images/200003.jpg',
'./paddy-disease-classification/test_images/200004.jpg',
'./paddy-disease-classification/test_images/200005.jpg',
'./paddy-disease-classification/test_images/200006.jpg',
'./paddy-disease-classification/test_images/200007.jpg',
'./paddy-disease-classification/test_images/200008.jpg',
'./paddy-disease-classification/test_images/200009.jpg',
'./paddy-disease-classification/test_images/200010.jpg']
submission_csv.drop(columns=['file_path'],inplace=True)
submission_csv.head()
| image_id | label | |
|---|---|---|
| 0 | 200001.jpg | NaN |
| 1 | 200002.jpg | NaN |
| 2 | 200003.jpg | NaN |
| 3 | 200004.jpg | NaN |
| 4 | 200005.jpg | NaN |
- 찾은 Model을 Load하여 Test Image를 Predict합니다.
best_model_chkpoint = "CheckPoints_" + str(best_model_idx)
best_model_chkpoint
'CheckPoints_2'
model = load_model( best_model_chkpoint )
targets = []
for path in tqdm(file_path):
img = tf.io.read_file(path)
img = tf.image.decode_png(img, channels=3)
img = tf.image.resize(img, (224, 224))
img = tf.keras.applications.efficientnet_v2.preprocess_input(img)
img = tf.reshape(img , (-1,224,224,3))
pred = model.predict(img)
targets.append(le.inverse_transform([np.argmax(pred)])[0])
100%|██████████████████████████████████████████████████████████████████████████████| 3469/3469 [03:46<00:00, 15.35it/s]
targets[:10]
['hispa',
'normal',
'blast',
'blast',
'blast',
'brown_spot',
'dead_heart',
'brown_spot',
'hispa',
'normal']
submission_csv['label'] = targets
submission_csv[:10]
| image_id | label | |
|---|---|---|
| 0 | 200001.jpg | hispa |
| 1 | 200002.jpg | normal |
| 2 | 200003.jpg | blast |
| 3 | 200004.jpg | blast |
| 4 | 200005.jpg | blast |
| 5 | 200006.jpg | brown_spot |
| 6 | 200007.jpg | dead_heart |
| 7 | 200008.jpg | brown_spot |
| 8 | 200009.jpg | hispa |
| 9 | 200010.jpg | normal |
submission_csv.to_csv("submission.csv",index=False)
- 점수는 0.9454가 나왔네요.