kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
13,020,876 | list_geres_name=['Comedy','Thriller','Action','Drama','Romance']<feature_engineering> | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head() | Cassava Leaf Disease Classification |
13,020,876 | for i in list_geres_name:
new_data['geres_name'+'_'+i]=new_data['geres_name'].apply(lambda x: 1 if i in x else 0 )<drop_column> | train.label.value_counts() | Cassava Leaf Disease Classification |
13,020,876 | new_data=new_data.drop('geres_name',axis=1 )<data_type_conversions> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
13,020,876 | new_data['production_countries']=new_data['production_countries'].fillna('QQ' )<feature_engineering> | class CassavaDataset(Dataset):
def __init__(
self, df, data_root, transforms=None, output_label=True
):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem__(sel... | Cassava Leaf Disease Classification |
13,020,876 | new_data['production_countries'].loc[new_data['production_countries']=='Unknow']['production_countries']='QQ'<count_unique_values> | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
IAASharpen, IAAEmboss, RandomBrightnessCon... | Cassava Leaf Disease Classification |
13,020,876 | list_pro_coun=list(new_data['production_countries'])
d = Counter([j for i in list_pro_coun for j in i] ).most_common(5)
d<feature_engineering> | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | Cassava Leaf Disease Classification |
13,020,876 | for i in d:
new_data['production_cou_name'+'_'+i[0]]=new_data['production_countries'].apply(lambda x: 1 if i[0] in x else 0 )<feature_engineering> | if __name__ == '__main__':
seed_everything(CFG['seed'])
test = pd.DataFrame()
test['image_id'] = list(os.listdir('.. /input/cassava-leaf-disease-classification/test_images/'))
final_vals = []
final_preds = []
for cfg in CFGs:
test_ds = CassavaDataset(test, '.. /input/cassava-leaf-disease-classification/test_images/', ... | Cassava Leaf Disease Classification |
13,020,876 | new_data['pro_country_count']=new_data['production_companies'].apply(lambda x:len(x))<groupby> | test['label'] = np.argmax(final_preds/len(CFGs), axis=1)
print(test.head())
test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,080,105 | pro_count_rev=new_data.loc[train.index].groupby('pro_country_count' ).revenue.median()<drop_column> | ImageFile.LOAD_TRUNCATED_IMAGES = True
warnings.simplefilter('ignore')
%matplotlib inline | Cassava Leaf Disease Classification |
13,080,105 | new_data=new_data.drop('production_countries',axis=1 )<categorify> | n_epochs = 10
n_patience = 5
n_folds = 3
train_bsize = 24
valid_bsize = 48
test_bsize = 48
seed = 42
effnet_output = {0: 1280, 1: 1280, 2: 1408, 3: 1536, 4: 1792, 5: 2048, 6: 2304, 7: 2560}
IMG_SIZE = 512
EFFNET_MODEL = 4
AUGMENTATION =[albumentations.ShiftScaleRotate(shift_limit=0.2, scale_limit=0.2, rotate_limit=15, ... | Cassava Leaf Disease Classification |
13,080,105 | new_data['production_companies'].loc[new_data['production_companies']=='unknow']='u'<drop_column> | path = '.. /input/cassava-leaf-disease-classification/'
trained_path = '.. /input/cassava-b4-512-final/' | Cassava Leaf Disease Classification |
13,080,105 | d.remove(( 'u',414))
<concatenate> | df = pd.read_csv(path + 'train.csv')
N_CLASSES = df.label.nunique() | Cassava Leaf Disease Classification |
13,080,105 | dd=[]
for i in d:
dd.append(i[0] )<concatenate> | class ClassificationDataset:
def __init__(self, image_paths, targets, resize, augmentations=None):
self.image_paths = image_paths
self.targets = targets
self.resize = resize
self.augmentations = augmentations
def __len__(self):
return len(self.image_paths)
def __getitem__(self, item):
image = Image.open(self.image_pat... | Cassava Leaf Disease Classification |
13,080,105 | dd=[]
for i in d:
dd.append(i[0] )<drop_column> | class Engine:
@staticmethod
def train(
data_loader,
model,
optimizer,
device,
scheduler=None,
accumulation_steps=1,
fp16=True,
):
losses = AverageMeter()
accuracies = AverageMeter()
final_predictions = []
model.train()
if accumulation_steps > 1:
optimizer.zero_grad()
if fp16:
scaler = torch.cuda.amp.GradScaler()
for ... | Cassava Leaf Disease Classification |
13,080,105 | new_data=new_data.drop('spoken_languages',axis=1 )<feature_engineering> | class EfficientNet(nn.Module):
def __init__(self, num_classes):
super(EfficientNet, self ).__init__()
self.base_model = timm.create_model(f"tf_efficientnet_b{str(EFFNET_MODEL)}_ns", pretrained=False)
self.dropout = nn.Dropout(0.2)
self.out = nn.Linear(
in_features=effnet_output[EFFNET_MODEL],
out_features=num_classe... | Cassava Leaf Disease Classification |
13,080,105 | new_data['runtime'].loc[new_data['runtime']==0]=1
new_data['time_budget']=new_data['budget'].fillna(0)/new_data['runtime'].fillna(1 )<drop_column> | test = pd.read_csv(path + "sample_submission.csv" ) | Cassava Leaf Disease Classification |
13,080,105 | <drop_column><EOS> | final_preds = None
for i in range(n_folds):
preds = predict(fold = i, apply_tta=IS_TTA)
temp_preds = None
for p in preds:
if temp_preds is None:
temp_preds = p
else:
temp_preds = np.vstack(( temp_preds, p))
if final_preds is None:
final_preds = temp_preds
else:
final_preds += temp_preds
final_preds /= n_folds
final_pr... | Cassava Leaf Disease Classification |
13,081,575 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | print("Tensorflow version " + tf.__version__ ) | Cassava Leaf Disease Classification |
13,081,575 | for i in list1:
new_data['title_is'+i]=new_data['title'].fillna('' ).apply(lambda x: 1 if i in x else 0 )<feature_engineering> | AUTOTUNE = tf.data.experimental.AUTOTUNE
GCS_PATH = ".. /input/cassava-leaf-disease-classification"
GCS_PATH_STRATIFICATED =".. /input/cassava-recreate-stratificated-tfrecords"
REPLICAS = strategy.num_replicas_in_sync
BATCH_SIZE = 128
AUG_BATCH = BATCH_SIZE
IMAGE_SIZE = [512, 512]
DIM = IMAGE_SIZE[0]
CLASSES = ['0', '1... | Cassava Leaf Disease Classification |
13,081,575 | for col in ['tagline', 'overview','title']:
new_data['len_' + col] =new_data[col].fillna('' ).apply(lambda x: len(x))
new_data['words_' + col] = new_data[col].fillna('' ).apply(lambda x: len(x.split(' ')))
new_data=new_data.drop(col,axis=1 )<feature_engineering> | test_df = pd.read_csv(GCS_PATH + '/sample_submission.csv')
train_df = pd.read_csv(GCS_PATH + '/train.csv' ) | Cassava Leaf Disease Classification |
13,081,575 | new_data['budget_popularity']=new_data['budget']*1.0/new_data['popularity']<count_values> | files_test = np.sort(np.array(tf.io.gfile.glob(GCS_PATH + '/test_tfrecords/*.tfrec')) ) | Cassava Leaf Disease Classification |
13,081,575 | a=new_data.groupby(['release_year'] ).release_month.value_counts()
b=new_data.groupby(['release_year','release_month'] ).release_day.value_counts()
new_data[(new_data['release_year']==2017)&(new_data['release_day']==30)]
<categorify> | ROT_ = 180.0
SHR_ = 2.0
HZOOM_ = 8.0
WZOOM_ = 8.0
HSHIFT_ = 8.0
WSHIFT_ = 8.0 | Cassava Leaf Disease Classification |
13,081,575 | new_data['_releaseYear_popularity_ratio'] = new_data['release_year'] / new_data['popularity']
new_data['_releaseYear_popularity_ratio2'] = new_data['popularity'] / new_data['release_year']
new_data['runtime_to_mean_year'] = new_data['runtime'] / new_data.groupby("release_year")["runtime"].transform('mean')
new_data['p... | def get_mat(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = math.pi * rotation / 180.
shear = math.pi * shear / 180.
def get_3x3_mat(lst):
return tf.reshape(tf.concat([lst],axis=0), [3,3])
c1 = tf.math.cos(rotation)
s1 = tf.math.sin(rotation)
one = tf.constant([1],dtype='float32')
... | Cassava Leaf Disease Classification |
13,081,575 |
<feature_engineering> | def to_float32(image, label):
return tf.cast(image, tf.float32), label | Cassava Leaf Disease Classification |
13,081,575 |
<split> | def decode_image(image):
image = tf.image.decode_jpeg(image, channels=3)
image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [*IMAGE_SIZE, 3])
return image | Cassava Leaf Disease Classification |
13,081,575 | new_train=new_data.loc[train.index]
new_test=new_data.loc[test.index]<train_model> | def read_labeled_tfrecord(example):
tfrec_format = {
'image' : tf.io.FixedLenFeature([], tf.string),
'target' : tf.io.FixedLenFeature([], tf.int64)
}
example = tf.io.parse_single_example(example, tfrec_format)
return example['image'], example['target'] | Cassava Leaf Disease Classification |
13,081,575 | x=new_train.drop('revenue',axis=1)
y=new_train['revenue']
x=x.fillna(0)
decision_tree = DecisionTreeRegressor(max_depth = 6)
decision_tree.fit(x, y)
<train_model> | def read_tfrecord(example, labeled):
tfrecord_format = {
"image": tf.io.FixedLenFeature([], tf.string),
"target": tf.io.FixedLenFeature([], tf.int64)
} if labeled else {
"image": tf.io.FixedLenFeature([], tf.string),
"image_name": tf.io.FixedLenFeature([], tf.string)
}
example = tf.io.parse_single_example(example, tf... | Cassava Leaf Disease Classification |
13,081,575 | x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
model_XG = xgboost.XGBRegressor()
model_XG.fit(x_train,y_train)
y_predict_rf = model_XG.predict(x_test)
print(mean_squared_error(y_test, y_predict_rf))
<drop_column> | def read_unlabeled_tfrecord(example, return_image_name):
tfrec_format = {
'image' : tf.io.FixedLenFeature([], tf.string),
'image_name' : tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, tfrec_format)
return example['image'], example['image_name'] if return_image_name else '0' | Cassava Leaf Disease Classification |
13,081,575 | X_test=new_test.drop('revenue',axis=1)
X_test=X_test.fillna(0 )<predict_on_test> | def load_dataset(filenames, labeled=True, ordered=False):
ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTOTUNE)
dataset = dataset.with_options(ignore_order)
dataset = dataset.map(partial(read_tfrecord,... | Cassava Leaf Disease Classification |
13,081,575 | ypred=model_XG.predict(X_test )<prepare_x_and_y> | TRAINING_FILENAMES = tf.io.gfile.glob('.. /input/cassava-leaf-disease-classification/train_tfrecords/' + '*train*.tfrec')
TEST_FILENAMES = tf.io.gfile.glob('.. /input/cassava-leaf-disease-classification/test_tfrecords/' + 'ld_test*.tfrec' ) | Cassava Leaf Disease Classification |
13,081,575 | dtrain = xgb.DMatrix(x, y)
dtest = xgb.DMatrix(X_test )<train_on_grid> | def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1)) for filename in filenames]
return np.sum(n)
NUM_TRAINING_IMAGES = int(count_data_items(TRAINING_FILENAMES)*(FOLDS-1.) /FOLDS)
NUM_VALIDATION_IMAGES = int(count_data_items(TRAINING_FILENAMES)*(1./FOLDS))
NUM_TEST_IMAGES =... | Cassava Leaf Disease Classification |
13,081,575 | xgb_params = {
'eta': 0.08,
'max_depth': 15,
'subsample': 0.7,
'colsample_bytree': 0.7,
'objective': 'reg:linear',
'eval_metric': 'rmse',
'silent': 1
}
cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=1000, early_stopping_rounds=400, verbose_eval=50, show_stdv=False)
cv_output[['train-rmse-mean', 'test-rmse-mean... | def data_augment(image, label):
image = tf.image.random_flip_left_right(image)
return image, label | Cassava Leaf Disease Classification |
13,081,575 | y_predict1 = abs(model.predict(dtest))<prepare_x_and_y> | def get_test_dataset(ordered=False):
dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(AUTOTUNE)
return dataset | Cassava Leaf Disease Classification |
13,081,575 | train=x
y_train=np.log1p(y)
test=X_test
y=y_train<compute_train_metric> | def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1)) for filename in filenames]
return np.sum(n ) | Cassava Leaf Disease Classification |
13,081,575 | n_folds = 5
def rmsle_cv(model):
kf = KFold(n_folds, shuffle=True, random_state=42 ).get_n_splits(train.values)
rmse = np.sqrt(-cross_val_score(model, train.values,y_train.values, scoring="neg_mean_squared_error", cv=kf))
return(rmse)
def eval_model(model, name):
start_time = time.time()
score = rmsle_cv(model)
prin... | def onehot(image,label):
CLASSES = 5
return image,tf.one_hot(label,CLASSES ) | Cassava Leaf Disease Classification |
13,081,575 | mod_lasso = make_pipeline(RobustScaler() , Lasso(alpha=0.005, random_state=1))
eval_model(mod_lasso, "lasso")
mod_enet = make_pipeline(RobustScaler() , ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))
eval_model(mod_enet, "enet")
mod_cat = CatBoostRegressor(iterations=10000, learning_rate=0.01,
depth=5, eval_me... | def get_validation_dataset(dataset, do_onehot=True):
dataset = dataset.batch(BATCH_SIZE)
if do_onehot: dataset = dataset.map(onehot, num_parallel_calls=AUTOTUNE)
dataset = dataset.cache()
dataset = dataset.prefetch(AUTOTUNE)
return dataset | Cassava Leaf Disease Classification |
13,081,575 | class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):
def __init__(self, base_models, meta_model, n_folds=5):
self.base_models = base_models
self.meta_model = meta_model
self.n_folds = n_folds
def fit(self, X, y):
self.base_models_ = [list() for x in self.base_models]
self.meta_model_ = clone(s... | def get_dataset(files, augment = False, shuffle = False, repeat = False,
labeled=True, return_image_names=False, batch_size=16, dim=512):
ds = tf.data.TFRecordDataset(files, num_parallel_reads=AUTOTUNE)
ds = ds.cache()
if repeat:
ds = ds.repeat()
if shuffle:
ds = ds.shuffle(1024*8)
opt = tf.data.Options()
opt.experim... | Cassava Leaf Disease Classification |
13,081,575 | def rmsle(y, y_pred):
return np.sqrt(mean_squared_error(y, y_pred))
def predict(model):
model.fit(train.values,y_train.values)
train_pred = model.predict(train.values)
pred = np.expm1(model.predict(test.values))
print(rmsle(y_train, train_pred))
return(pred )<predict_on_test> | class DataGenerator(Sequence):
def __init__(self, path, list_IDs, labels, batch_size, img_size, img_channel):
self.path = path
self.list_IDs = list_IDs
self.labels = labels
self.batch_size = batch_size
self.img_size = img_size
self.img_channel = img_channel
self.indexes = np.arange(len(self.list_IDs))
def __len__(self)... | Cassava Leaf Disease Classification |
13,081,575 | prediction1=0
prediction = predict(mod_lasso)
prediction = predict(mod_enet)
prediction = predict(mod_xgb)
prediction1+=prediction
prediction = predict(mod_gboost)
prediction1+=prediction
prediction = predict(mod_lgb)
prediction1+=prediction
prediction = predict(mod_stacked)
prediction1+=prediction
prediction<sav... | Cassava Leaf Disease Classification | |
13,081,575 | new_test['id']=new_test.index
new_test['revenue']=prediction
new_test[['id','revenue']].to_csv('submission_Dragon2.csv', index=False)
new_test[['id','revenue']].head()<save_to_csv> | test_generator = DataGenerator('.. /input/cassava-leaf-disease-classification/'+'test_images/', test_df['image_id'], test_df['label'], 1, DIM, 3 ) | Cassava Leaf Disease Classification |
13,081,575 | new_test['revenue']=prediction1/4
new_test[['id','revenue']].to_csv('submission_Dragon3.csv', index=False)
new_test[['id','revenue']].head()<define_variables> |
BASE_WEIGHTS_PATH = 'https://storage.googleapis.com/keras-applications/'
WEIGHTS_HASHES = {
'b0':('902e53a9f72be733fc0bcb005b3ebbac',
'50bc09e76180e00e4465e1a485ddc09d'),
'b1':('1d254153d4ab51201f1646940f018540',
'74c4e6b3e1f6a1eea24c589628592432'),
'b2':('b15cce36ff4dcbd00b6dd88e7857a6ad',
'111f8e2ac8aa800a7a99e3239... | Cassava Leaf Disease Classification |
13,081,575 | package_path = '.. /input/vision-transformer-pytorch/VisionTransformer-Pytorch'
sys.path.append(package_path)
<define_variables> | def get_model(weights='imagenet'):
inp = tf.keras.layers.Input(shape=(DIM,DIM,3))
base = EfficientNetB0(input_shape=(DIM,DIM,3),weights=None,include_top=False)
x = base(inp)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(5,activation='softmax' )(x)
model ... | Cassava Leaf Disease Classification |
13,081,575 | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
sys.path.append(package_path )<import_modules> | skf = KFold(n_splits=FOLDS,shuffle=True,random_state=12)
for fold,(idxT,idxV)in enumerate(skf.split(np.arange(5))):
if fold==(FOLDS-1):
idxTT = idxT; idxVV = idxV
print('
print('Fold',fold,'has TRAIN:',idxT,'VALID:',idxV ) | Cassava Leaf Disease Classification |
13,081,575 | from glob import glob
from sklearn.model_selection import GroupKFold, StratifiedKFold
import cv2
from skimage import io
import torch
from torch import nn
import os
from datetime import datetime
import time
import random
import cv2
import torchvision
from torchvision import transforms
import pandas as pd
import numpy as... | pred = np.zeros(( test_df.shape[0],5))
for fold,(idxT,idxV)in enumerate(skf.split(np.arange(5))):
print() ; print('
print('
print('
files_train = tf.io.gfile.glob([GCS_PATH_STRATIFICATED + '/train%.2i*.tfrec'%x for x in idxT])
files_valid = tf.io.gfile.glob([GCS_PATH_STRATIFICATED + '/train%.2i*.tfrec'%x for x in idxV... | Cassava Leaf Disease Classification |
13,081,575 | CFG = {
'fold_num': 10,
'seed': 719,
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 384,
'epochs': 60,
'train_bs': 28,
'valid_bs': 32,
'lr': 1e-2,
'num_workers': 5,
'accum_iter': 1,
'verbose_step': 2,
'device': 'cuda:0',
'tta': 6,
'used_epochs': [6,7,8,9],
'weights': [1,1,1,1]
}<load_from_csv> | ds = get_dataset(files_test, augment=False, repeat=False, dim=IMAGE_SIZE[0],
labeled=False, return_image_names=True)
image_names = np.array([img_name.numpy().decode("utf-8")
for img, img_name in iter(ds.unbatch())] ) | Cassava Leaf Disease Classification |
13,081,575 | <count_values><EOS> | prediction = np.argmax(pred, axis=1)
test_df['label'] = prediction
test_df = test_df[["image_id","label"]]
test_df.to_csv('submission.csv',index=False)
test_df | Cassava Leaf Disease Classification |
13,857,194 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<load_from_csv> | !pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git | Cassava Leaf Disease Classification |
13,857,194 | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head()<categorify> | def seed_everything(seed=0):
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
seed = 0
seed_everything(seed)
warnings.filterwarnings('ignore' ) | Cassava Leaf Disease Classification |
13,857,194 | class CassavaDataset(Dataset):
def __init__(
self, df, data_root, transforms=None, output_label=True
):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem__(sel... | try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print(f'Running on TPU {tpu.master() }')
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distr... | Cassava Leaf Disease Classification |
13,857,194 | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
IAASharpen, IAAEmboss, RandomBrightnessCon... | BATCH_SIZE = 16 * REPLICAS
HEIGHT = 512
WIDTH = 512
CHANNELS = 3
N_CLASSES = 5
TTA_STEPS = 3 | Cassava Leaf Disease Classification |
13,857,194 | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | database_base_path = '/kaggle/input/cassava-leaf-disease-classification/'
submission = pd.read_csv(f'{database_base_path}sample_submission.csv')
display(submission.head())
TEST_FILENAMES = tf.io.gfile.glob(f'{database_base_path}test_tfrecords/ld_test*.tfrec')
NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
print... | Cassava Leaf Disease Classification |
13,857,194 | if __name__ == '__main__':
seed_everything(CFG['seed'])
folds = StratifiedKFold(n_splits=CFG['fold_num'] ).split(np.arange(train.shape[0]), train.label.values)
for fold,(trn_idx, val_idx)in enumerate(folds):
if fold > 0:
break
print('Inference fold {} started'.format(fold))
valid_ = train.loc[val_idx,:].reset_index(d... | model_path_list = glob.glob('/kaggle/input/cassava-leaf-disease-tpu-tensorflow-training/*.h5')
model_path_list.sort()
print('Models to predict:')
print(*model_path_list, sep='
' ) | Cassava Leaf Disease Classification |
13,857,194 | test['label'] = np.argmax(tst_preds, axis=1)
test.head()<save_to_csv> | model_path_list_2 = glob.glob('/kaggle/input/cassava-leaf-disease-training-with-tpu-v2-pods/*.h5')
model_path_list_2.sort()
print('Models to predict:')
print(*model_path_list_2, sep='
' ) | Cassava Leaf Disease Classification |
13,857,194 | test.to_csv('submission.csv', index=False )<install_modules> | def model_fn(input_shape, N_CLASSES):
inputs = L.Input(shape=input_shape, name='inputs')
base_model = efn.EfficientNetB3(input_tensor=inputs,
include_top=False,
weights=None,
pooling='avg')
model = tf.keras.Sequential([
base_model,
L.Dropout (.25),
L.Dense(N_CLASSES, activation='softmax', name='output')
])
return m... | Cassava Leaf Disease Classification |
13,857,194 | !pip install.. /input/timm034/timm-0.3.4-py3-none-any.whl<import_modules> | files_path = f'{database_base_path}test_images/'
test_preds = np.zeros(( len(os.listdir(files_path)) , N_CLASSES))
print('First model')
for model_path in model_path_list:
print(model_path)
K.clear_session()
model.load_weights(model_path)
if TTA_STEPS > 0:
test_ds = get_dataset(files_path, tta=True)
for step in rang... | Cassava Leaf Disease Classification |
13,857,194 | <define_variables><EOS> | submission = pd.DataFrame({'image_id': image_names, 'label': test_preds})
submission.to_csv('submission.csv', index=False)
display(submission.head() ) | Cassava Leaf Disease Classification |
14,111,611 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<define_variables> | !pip install --quiet.. /input/kerasapplications/keras-team-keras-applications-3b180cb
!pip install --quiet /kaggle/input/efficientnet-git | Cassava Leaf Disease Classification |
14,111,611 | ls.. /input/cdl-cspresnext50-512/<define_variables> | Flatten,GlobalAveragePooling2D,BatchNormalization, Activation
print(tf.__version__ ) | Cassava Leaf Disease Classification |
14,111,611 | model_pths = [
'.. /input/cdl-cspresnext50-512/light_best_model_fold0.pth',
'.. /input/cdl-cspresnext50-512/light_best_model_fold1.pth',
'.. /input/cdl-cspresnext50-512/light_best_model_fold2.pth',
'.. /input/cdl-cspresnext50-512/light_best_model_fold3.pth',
'.. /input/cdl-cspresnext50-512/light_best_model_fold4.pth',
... | try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print(f'Running on TPU {tpu.master() }')
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distr... | Cassava Leaf Disease Classification |
14,111,611 | class net(nn.Module):
def __init__(self, model_name=enet_type, pretrained=False):
super().__init__()
self.model = timm.create_model(model_name, pretrained=pretrained)
n_features = self.model.head.fc.in_features
self.model.head.fc = nn.Linear(n_features, 5)
def forward(self, x):
output = self.model(x)
return output<c... | SEED = 100
DEBUG = False
WANDB = False
VALIDATION_SIZE = 0.2
BATCH_SIZE = 32 *REPLICAS
LEARNING_RATE = 3e-5 * REPLICAS
EPOCHS=40
MODEL_NAME = "EfficentNetB4"
N_FOLDS = 5
TTA = True
N_TTA = 7
T_1 = 0.2
T_2 = 1.2
SMOOTH_FRACTION = 0.01
N_ITER = 5
HEIGHT = 512
WIDTH = 512
HEIGHT_RS = 512
WIDTH_RS = 512
CHANNELS = 3
N_CLAS... | Cassava Leaf Disease Classification |
14,111,611 | class LEAFDataset(Dataset):
def __init__(self, folder, transforms=None):
self.file_names = os.listdir(folder)
self.transforms = transforms
def __len__(self):
return len(self.file_names)
def __getitem__(self, index):
image_id = self.file_names[index]
image_file = os.path.join(image_folder, image_id)
image = cv2.imrea... | def transform_rotation(image, height, rotation):
DIM = height
XDIM = DIM%2
rotation = rotation * tf.random.uniform([1],dtype='float32')
rotation = math.pi * rotation / 180.
c1 = tf.math.cos(rotation)
s1 = tf.math.sin(rotation)
one = tf.constant([1],dtype='float32')
zero = tf.constant([0],dtype='float32')
rotation... | Cassava Leaf Disease Classification |
14,111,611 | transform = albumentations.Compose([
albumentations.Resize(image_size, image_size),
albumentations.Normalize() ,
ToTensorV2()
] )<load_pretrained> | def data_augment(image, label):
p_rotation = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
p_spatial = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
p_rotate = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
p_pixel_1 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
p_pixel_2 = tf.random.uniform([], 0, 1.0, dt... | Cassava Leaf Disease Classification |
14,111,611 | res = []
for model_pth in model_pths:
model = net(enet_type)
model.load_state_dict(torch.load(model_pth))
model.eval()
model.to(device)
test_dataset = LEAFDataset(image_folder, transforms=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, num_workers=num_workers)
single_model_... | copyfile(src = ".. /input/bitempered-logistic-loss-tensorflow-v2/bi_tempered_loss.py", dst = ".. /working/loss.py")
| Cassava Leaf Disease Classification |
14,111,611 | sub = pd.DataFrame({'image_id': image_ids_list, 'label': probs});sub.head()<save_to_csv> | with strategy.scope() :
class BiTemperedLogisticLoss(tf.keras.losses.Loss):
def __init__(self, t1, t2, lbl_smth, n_iter):
super(BiTemperedLogisticLoss, self ).__init__()
self.t1 = t1
self.t2 = t2
self.lbl_smth = lbl_smth
self.n_iter = n_iter
def call(self, y_true, y_pred):
return bi_tempered_logistic_loss(y_pred, y_tru... | Cassava Leaf Disease Classification |
14,111,611 | sub.to_csv('submission.csv', index=False )<save_to_csv> | files_path = '.. /input/cassava-leaf-disease-classification/test_images'
TEST_FILENAMES = tf.io.gfile.glob('.. /input/cassava-leaf-disease-classification/test_tfrecords/*')
model_base_path = '.. /input/cassava-efficientnetb4'
model_path_list = os.listdir(model_base_path)
model_path_list = ['EfficentNet4_best_btl_fold... | Cassava Leaf Disease Classification |
14,111,611 | <define_variables><EOS> | test_preds = np.argmax(test_preds, axis=-1)
image_names = [img_name.numpy().decode('utf-8')for img, img_name in iter(test_ds.unbatch())]
submission = pd.DataFrame({'image_id': image_names, 'label': test_preds})
submission.to_csv('submission.csv', index=False)
display(submission.head() ) | Cassava Leaf Disease Classification |
13,057,068 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<define_variables> | sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path
sys.path = [
'.. /input/ttach-kaggle/ttach/',
] + sys.path
| Cassava Leaf Disease Classification |
13,057,068 | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
sys.path.append(package_path )<import_modules> | warnings.filterwarnings('ignore' ) | Cassava Leaf Disease Classification |
13,057,068 | from glob import glob
from sklearn.model_selection import GroupKFold, StratifiedKFold
import cv2
from skimage import io
import torch
from torch import nn
import os
from datetime import datetime
import time
import random
import cv2
import torchvision
from torchvision import transforms
import pandas as pd
import numpy as... | DIR_INPUT = '/kaggle/input/cassava-leaf-disease-classification'
DIR_WEIGHTS = '/kaggle/input/cassava-pytorch-starter-train'
SEED = 42
N_FOLDS = 1
BATCH_SIZE = 16
SIZE = 512
CROP = 512
init_lr = 5e-5
n_epochs = 5 | Cassava Leaf Disease Classification |
13,057,068 | CFG = {
'fold_num': 10,
'seed': 719,
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 384,
'epochs': 100,
'train_bs': 28,
'valid_bs': 32,
'lr': 1e-2,
'num_workers': 10,
'accum_iter': 1,
'verbose_step': 2,
'device': 'cuda:0',
'tta': 10,
'used_epochs': [6,7,8,9],
'weights': [1,1,1,1]
}<load_from_csv> | modelname="efficientnet-b0"
modelname2="efficientnet-b2"
class enetv2(nn.Module):
def __init__(self, out_dim=1, ModelName="efficientnet-b0"):
super(enetv2, self ).__init__()
self.basemodel = EfficientNet.from_name(ModelName)
self.myfc = nn.Linear(self.basemodel._fc.in_features, out_dim)
self.basemodel._fc = nn.Identi... | Cassava Leaf Disease Classification |
13,057,068 | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head()<count_values> | transforms_test = A.Compose([
A.Resize(height=SIZE, width=SIZE, p=1.0),
] ) | Cassava Leaf Disease Classification |
13,057,068 | train.label.value_counts()<load_from_csv> | submission_df = pd.read_csv(DIR_INPUT + '/sample_submission.csv')
submission_df.iloc[:, 1] = 0
submission_df.head() | Cassava Leaf Disease Classification |
13,057,068 | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head()<categorify> | if submission_df.shape[0] == 1:
submission_df = pd.DataFrame([{'image_id': '2216849948.jpg', 'label': 0},{'image_id': '2216849948.jpg', 'label': 0}])
submission_df.reset_index(drop=True, inplace=True)
commit = True
else:
commit = False
submission_df.head() | Cassava Leaf Disease Classification |
13,057,068 | class CassavaDataset(Dataset):
def __init__(
self, df, data_root, transforms=None, output_label=True
):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem__(sel... | dataset_test = CassavaDataset(df=submission_df, dataset='test', transforms=transforms_test)
dataloader_test = DataLoader(dataset_test, batch_size=BATCH_SIZE, num_workers=4, shuffle=False ) | Cassava Leaf Disease Classification |
13,057,068 | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
IAASharpen, IAAEmboss, RandomBrightnessCon... | submissions = None
device = torch.device("cuda:0")if torch.cuda.is_available() else torch.device('cpu')
for i_fold in range(N_FOLDS):
model = enetv2(5, modelname2 ).to(device)
model.to(device)
checkpoint2 = torch.load(f".. /input/cassavadata/efficientnet-b2_512_final_epoch10_fold0.pth", map_location=device)
model.l... | Cassava Leaf Disease Classification |
13,057,068 | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | pl_df = pd.read_csv(DIR_INPUT + '/sample_submission.csv')
if pl_df.shape[0] == 1:
pl_df = pd.DataFrame([{'image_id': '2216849948.jpg', 'label': 0},{'image_id': '2216849948.jpg', 'label': 0}])
pl_df.reset_index(drop=True, inplace=True)
pl_df['label'] = torch.argmax(submissions, dim=1)
pl_df["pl"] = np.ones_like(torc... | Cassava Leaf Disease Classification |
13,057,068 | if __name__ == '__main__':
seed_everything(CFG['seed'])
folds = StratifiedKFold(n_splits=CFG['fold_num'] ).split(np.arange(train.shape[0]), train.label.values)
for fold,(trn_idx, val_idx)in enumerate(folds):
if fold > 0:
break
print('Inference fold {} started'.format(fold))
valid_ = train.loc[val_idx,:].reset_index(d... | df_train = pd.read_csv(os.path.join(DIR_INPUT,"train.csv"))
df_train["pl"] = np.zeros_like(df_train["image_id"])
df_train = pd.concat([df_train, pl_df] ).reset_index() | Cassava Leaf Disease Classification |
13,057,068 | test['label'] = np.argmax(tst_preds, axis=1)
test.head()<save_to_csv> | class CassavaDataset2(Dataset):
def __init__(self, df, dataset='train', transforms=None):
self.df = df
self.transforms=transforms
self.dataset=dataset
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
imageid = self.df.loc[idx, "image_id"]
label = self.df.loc[idx, "label"]
dir = self.df.loc[idx, "p... | Cassava Leaf Disease Classification |
13,057,068 | test.to_csv('submission.csv', index=False )<define_variables> | scaler = torch.cuda.amp.GradScaler(enabled=False)
def train_epoch(loader, optimizer):
model.train()
train_loss = []
bar = tqdm(loader)
i = 0
for(data, target)in bar:
data, target = data.to(device), target.to(device ).long()
loss_func = criterion
optimizer.zero_grad()
with torch.cuda.amp.autocast(enabled=False):
logit... | Cassava Leaf Disease Classification |
13,057,068 | tez_path = '.. /input/tez-lib/'
effnet_path = '.. /input/efficientnet-pytorch/'
timm_path = '.. /input/timm-pytorch-image-models/pytorch-image-models-master'
sys.path.append(tez_path)
sys.path.append(effnet_path)
sys.path.append(timm_path )<import_modules> | for epoch in range(1, n_epochs+1):
torch.cuda.empty_cache()
scheduler.step(epoch-1)
train_loss = train_epoch(dataloader_train , optimizer ) | Cassava Leaf Disease Classification |
13,057,068 | import os
import albumentations
import pandas as pd
import numpy as np
import timm
import tez
from tez.datasets import ImageDataset
import torch
import torch.nn as nn
from torch.nn import functional as F
from tqdm import tqdm
from efficientnet_pytorch import EfficientNet<feature_engineering> | submissions = None
device = torch.device("cuda:0")if torch.cuda.is_available() else torch.device('cpu')
for i_fold in range(N_FOLDS):
model.eval()
transforms = tta.Compose(
[
tta.HorizontalFlip() ,
]
)
tta_models = []
for model in [model]:
tta_models.append(tta.ClassificationTTAWrapper(model, transforms))
for net i... | Cassava Leaf Disease Classification |
13,057,068 | <choose_model_class><EOS> | submission_df['label'] = torch.argmax(submissions, dim=1)
submission_df.to_csv('submission.csv', index=False)
submission_df | Cassava Leaf Disease Classification |
14,109,551 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<set_options> | !pip install.. /input/vision-transformer/vision_transformer_pytorch-1.0.2-py2.py3-none-any.whl | Cassava Leaf Disease Classification |
14,109,551 | img_size = 512
test_aug = albumentations.Compose([
albumentations.RandomResizedCrop(img_size, img_size),
albumentations.Transpose(p=0.5),
albumentations.HorizontalFlip(p=0.5),
albumentations.VerticalFlip(p=0.5),
albumentations.HueSaturationValue(
hue_shift_limit=0.2,
sat_shift_limit=0.2,
val_shift_limit=0.2,
p=0.5
),... | model = VisionTransformer.from_name('ViT-B_16', num_classes=5)
model.load_state_dict(torch.load('.. /input/vitb16trained/ViT-B_16_trained.pt',map_location=torch.device('cpu')) ) | Cassava Leaf Disease Classification |
14,109,551 | dfx = pd.read_csv(".. /input/cassava-leaf-disease-classification/sample_submission.csv")
image_path = ".. /input/cassava-leaf-disease-classification/test_images/"
test_image_paths = [os.path.join(image_path, x)for x in dfx.image_id.values]
test_targets = dfx.label.values
test_dataset = ImageDataset(
image_paths=test_... | test_df = pd.read_csv(".. /input/cassava-leaf-disease-classification/sample_submission.csv")
image_path = ".. /input/cassava-leaf-disease-classification/test_images/"
test_targets = test_df.label.values
test_aug = albu.Compose([
albu.CenterCrop(512, 512, p=1.) ,
albu.Resize(384, 384),
albu.Normalize(
mean=[0.485, 0.4... | Cassava Leaf Disease Classification |
14,109,551 | train_dfx = pd.read_csv(".. /input/cassava-leaf-disease-classification/train.csv")
model_path = ".. /input/cassava-model-3"
model0 = EfficientnetModel(num_classes=train_dfx.label.nunique())
model0.load(f"{model_path}/efficentnet_model_fold0.bin", device='cuda')
model1 = EfficientnetModel(num_classes=train_dfx.label.... | device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
predictions=[]
for imgs in test_loader:
imgs = imgs.to(device)
with torch.no_grad() :
model=model.to(device)
outputs = model(imgs)
_, predicted = torch.max(outputs, dim=1)
predicted=predicted.to('cpu')
predictions.append(predicted ) | Cassava Leaf Disease Classification |
14,109,551 | <feature_engineering><EOS> | test_df['label'] = np.concatenate(predictions)
test_df.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,418,167 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<define_variables> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
13,418,167 | def most_common(lst):
data = Counter(lst)
return max(lst, key=data.get)
final_preds = list()
for index, row in new_df.iterrows() :
out_list = [row['model0'], row['model1'], row['model2'], row['model3'], row['model4'],row['model5'], row['model6'], row['model7'], row['model8'], row['model9'] ]
final_preds.append(most_c... | from glob import glob
from sklearn.model_selection import GroupKFold, StratifiedKFold
import cv2
from skimage import io
import torch
from torch import nn
import os
from datetime import datetime
import time
import random
import cv2
import torchvision
from torchvision import transforms
import pandas as pd
import numpy as... | Cassava Leaf Disease Classification |
13,418,167 |
<predict_on_test> | CFG = {
'fold_num': 10,
'seed': 719,
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 512,
'epochs': 32,
'train_bs': 28,
'valid_bs': 32,
'lr': 1e-4,
'num_workers': 4,
'accum_iter': 1,
'verbose_step': 1,
'device': 'cuda:0',
'tta': 1,
'used_epochs': [6,7,8,9],
'weights': [1,1,1,1]
} | Cassava Leaf Disease Classification |
13,418,167 |
<save_to_csv> | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head() | Cassava Leaf Disease Classification |
13,418,167 | dfx.label = final_preds
dfx.to_csv("submission.csv", index=False )<define_variables> | train.label.value_counts() | Cassava Leaf Disease Classification |
13,418,167 | BATCH_SIZE = 1
image_size = 512
enet_type = ['tf_efficientnet_b4_ns'] * 5
model_path = ['.. /input/cassava-models-eff/baseline_cld_fold0_epoch8_tf_efficientnet_b4_ns_512.pth',
'.. /input/cassava-models-eff/baseline_cld_fold1_epoch9_tf_efficientnet_b4_ns_512.pth',
'.. /input/cassava-models-eff/baseline_cld_fold2_epoch9_... | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
13,418,167 | transforms_valid = albumentations.Compose([
albumentations.CenterCrop(image_size, image_size, p=1),
albumentations.Resize(image_size, image_size),
albumentations.Normalize()
] )<define_variables> | class CassavaDataset(Dataset):
def __init__(
self, df, data_root, transforms=None, output_label=True
):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem__(sel... | Cassava Leaf Disease Classification |
13,418,167 | OUTPUT_DIR = './'
MODEL_DIR = '.. /input/cassava-models-res/'
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
TRAIN_PATH = '.. /input/cassava-leaf-disease-classification/train_images'
TEST_PATH = '.. /input/cassava-leaf-disease-classification/test_images'<define_search_space> | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
IAASharpen, IAAEmboss, RandomBrightnessCon... | Cassava Leaf Disease Classification |
13,418,167 | class CFG:
debug=False
num_workers=8
model_name='resnext50_32x4d'
size=512
batch_size=32
seed=2020
target_size=5
target_col='label'
n_fold=5
trn_fold=[0, 1, 2, 3, 4]
inference=True<load_from_csv> | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | Cassava Leaf Disease Classification |
13,418,167 | test = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
test['filepath'] = test.image_id.apply(lambda x: os.path.join('.. /input/cassava-leaf-disease-classification/test_images', f'{x}'))
<create_dataframe> | if __name__ == '__main__':
seed_everything(CFG['seed'])
folds = StratifiedKFold(n_splits=CFG['fold_num'] ).split(np.arange(train.shape[0]), train.label.values)
for fold,(trn_idx, val_idx)in enumerate(folds):
if fold > 0:
break
print('Inference fold {} started'.format(fold))
valid_ = train.loc[val_idx,:].reset_index(d... | Cassava Leaf Disease Classification |
13,418,167 | test_dataset_efficient = CLDDataset(test, 'test', transform=transforms_valid)
test_loader_efficient = torch.utils.data.DataLoader(test_dataset_efficient, batch_size=BATCH_SIZE, shuffle=False, num_workers=4 )<categorify> | test['label'] = np.argmax(tst_preds, axis=1)
test.head() | Cassava Leaf Disease Classification |
13,418,167 | <choose_model_class><EOS> | test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
14,240,018 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<define_search_model> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
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