Kaggle competition: Google Landmark Recognition 2020
<SOS> metric: GoogleGlobalAP Kaggle data source: landmark-recognition-2020<count_missing_values>
!pip install.. /input/glrec2020/Keras_Applications-1.0.8-py3-none-any.whl
!pip install.. /input/glrec2020/efficientnet-1.1.0-py3-none-any.whl
sum(train.isna().sum())/(train.shape[0]*train.shape[1])*100<sort_values>
import operator
import gc
import pathlib
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import backend as K
from scipy import spatial
import cv2
import efficientnet.tfkeras as efn
import math
train_NA =(np.round(( train.isna().sum() /len(train)) ,3)).sort_values(ascending = False)*100
train_NA = train_NA.sort_values(ascending = False)
 train_NA.head(10 )<count_values>
NUMBER_OF_CLASSES = 81313
IMAGE_SIZE = [384, 384]
LR = 0.0001
train.dtypes.value_counts()<count_unique_values>
NUM_EMBEDDING_DIMENSIONS = 1024
DATASET_DIR = '.. /input/landmark-image-train/train_encoded.csv'
TEST_IMAGE_DIR = '.. /input/landmark-recognition-2020/test'
TRAIN_IMAGE_DIR = '.. /input/landmark-recognition-2020/train'
train.select_dtypes('category' ).apply(pd.Series.nunique, axis = 0 )<concatenate>
MODEL1 = get_model_B6()
MODEL2 = get_model_B7()
MODEL1.load_weights('.. /input/effb6-512-ep18/effb6model512-18.h5')
 MODEL1 = tf.keras.models.Model(inputs = MODEL1.input[0], outputs = MODEL1.layers[-4].output)
 MODEL2.load_weights('.. /input/effb7-512-ep12/effb7model512-12.h5')
 MODEL2 = tf.keras.models.Model(inputs = MODEL2.input[0], outputs = MODEL2.layers[-4].output )
data = train.append(test )<count_missing_values>
NUM_TO_RERANK = 3
NUM_PUBLIC_TEST_IMAGES = 10345
<filter><EOS> 
final = inference_and_save_submission_csv(DATASET_DIR, TEST_IMAGE_DIR, TRAIN_IMAGE_DIR )