Day #300 - Lessons Learnt from Multi-Label Classification

Today is 300th Post on Data Science. It has been a long journey. Still I feel there is a lot more to catchup. Keep Learning, Keep Going.

There are different tasks involved

1. Data Collection - Fatkun Batch Download Image chrome extension to download images
2. Script to reshape images and store in a standard format
3. Simple DB script to update and prepare data

	import pyodbc
	import os
	files = os.listdir(r'E:\Multi_Label\Input_Data\T-Shirt_Jeans')
	base_filepath = r'E:\Multi_Label\Input_Data\T-Shirt_Jeans'
	cnxn = pyodbc.connect(r'DRIVER={SQL SERVER};SERVER=XXXXX\SQLEXPRESS;DATABASE=DataGeneration;Trusted_Connection=yes;')
	cursor = cnxn.cursor()
	for file in files:
	filepath = base_filepath + '\\' + file
	cursor.execute("insert into Dataset([FileName],[Cap],[Jeans],[Jacket],[TShirt],[Shirt],[Pants]) values (?,0,1,0,1,0,0)",filepath)
	cnxn.commit()
	cnxn.close()

view raw pyodbc_insert.sql hosted with ❤ by GitHub

4. This base implementation was useful for model implementation link
5. Data Test Results

	#Test Code
	import cv2
	import os
	import numpy as np
	from keras.models import save_model, load_model
	test_dataset = r'E:\Multi_Label\Test'
	model = load_model(r'E:\Multi_Label\\model_multi_label.h5')
	print(model.summary())
	test_images = []
	arr = os.listdir(test_dataset)
	files = []
	for file in arr:
	path = test_dataset + '\\'+file
	img = cv2.imread(path,1)
	img = cv2.resize(img,(256,256))
	test_images.append([np.array(img)])
	files.append(path)
	i = 0
	for data in test_images:
	test_img_data = np.array(data).reshape(-1,256,256,3)
	result = model.predict(test_img_data)
	#print(result)
	item = ['1-Cap','2 Jeans','3 - Jacket','4-TShirt','5-Shirt','6-Pants']
	values = []
	values.append(result[0][0])
	values.append(result[0][1])
	values.append(result[0][2])
	values.append(result[0][3])
	values.append(result[0][4])
	values.append(result[0][5])
	print(files[i])
	if(float(result[0][0])>0.5):
	print('Cap')
	if(float(result[0][1])>0.5):
	print('Jeans')
	if(float(result[0][2])>0.5):
	print('Jacket')
	if(float(result[0][3])>0.5):
	print('Tshirt')
	if(float(result[0][4])>0.5):
	print('Shirt')
	if(float(result[0][5])>0.5):
	print('Pants')
	#print(values)
	i = i+1
	#E:\Multi_Label\Test\Cap_Jean.jpeg
	#Cap
	#Jeans
	#E:\Multi_Label\Test\Cap_Jean_2.jpeg
	#Cap
	#Jeans
	#E:\Multi_Label\Test\Cap_Jean_3.jpeg
	#Cap
	#Jeans
	#E:\Multi_Label\Test\Jacket_5.jpg
	#Cap
	#Jeans
	#E:\Multi_Label\Test\Jacket_WM2.jpg
	#Cap
	#Jeans
	#Jacket
	#E:\Multi_Label\Test\Jacket_WM3.jpg
	#Cap
	#Jeans
	#Jacket
	#E:\Multi_Label\Test\Jacket_WM4.jpg
	#Cap
	#Jeans
	#E:\Multi_Label\Test\Jacket_WML1.jpg
	#Cap
	#Jeans
	#E:\Multi_Label\Test\T_Shirt_Target.jpg
	#Cap
	#Jeans
	#E:\Multi_Label\Test\T_Shirt_Target1.jpg
	#Cap
	#Jeans

view raw Test_Results.py hosted with ❤ by GitHub

Happy Learning!!!