FOODC
Jupyter Notebook
Here I have attached my notebook, for the reference of the people who want to try their hands-on.
Here you will get to see the steps starting from Loading the dataset, resizing it and giving it to the pretrained model(VGG-16) for the training.
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
In [2]:
import os
import random
import cv2
import matplotlib.pyplot as plt
In [3]:
img_dir = r'C:\Users\KIIT\Documents\AICrowd\train_images'
In [4]:
df = pd.read_csv("train.csv")
df
Out[4]:
| ImageId | ClassName | |
|---|---|---|
| 0 | f27632d7e5.jpg | water |
| 1 | efa87919ed.jpg | pizza-margherita-baked |
| 2 | 4f169e8c8d.jpg | broccoli |
| 3 | a6956654bf.jpg | salad-leaf-salad-green |
| 4 | d99ce8c3bf.jpg | egg |
| ... | ... | ... |
| 9318 | ba8233c7d2.jpg | bread-french-white-flour |
| 9319 | 2090043907.jpg | wine-red |
| 9320 | 8762d1cefd.jpg | carrot |
| 9321 | 28e7439245.jpg | broccoli |
| 9322 | ba263cfb41.jpg | cucumber |
9323 rows × 2 columns
In [5]:
df_food_filename = df['ImageId']
df_food_filename[0]
df_food_name = df['ClassName']
In [6]:
img = df_food_filename[0]
image = cv2.imread(os.path.join(img_dir, img))
plt.imshow(image)
print(image.shape)
print(img)
(464, 464, 3) f27632d7e5.jpg
In [7]:
plt.figure(figsize=(8,8))
for i in range(9):
img = df_food_filename[i+9]
image = cv2.imread(os.path.join(img_dir, img))
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.subplot(3,3,i+1)
plt.imshow(image_rgb)
plt.xlabel('Food Name: {}\n'.format(df_food_name[i+9]))
plt.tight_layout()
In [8]:
a = np.array(df_food_filename)
paths = []
for i in range(len(a)):
img = a[i]
image = os.path.join(img_dir, img)
paths.append(image)
In [9]:
paths
Out[9]:
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'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\77d9b11ab6.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\e6c11abef2.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\9435be9788.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\6935be7d70.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\8c96ff00d7.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\606de246e5.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\ccaa17e93f.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\bdee00ca0f.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\27fee108e3.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\ebcc0f8125.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\8ab4fec3ba.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\575c9de3ff.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\fb3c2b3c8d.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\4260c39d10.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\c8e1a27a11.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\5d9b4a324d.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\cbf2caa83a.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\5e0c3a3438.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\a24a0421c2.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\a532ba16eb.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\6bfe54758a.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\eb44760694.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\857a408d76.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\eaf0bc62cb.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\cb5e199090.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\e87a71ea90.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\85a8173a8e.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\8d4c650171.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\c3605ec90c.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\61e5d5b3e1.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\bbe08a90eb.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\6b3d86f15e.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\050217baaf.jpg', 'C:\\Users\\KIIT\\Documents\\AICrowd\\train_images\\930c94b323.jpg', ...]
In [10]:
data = []
for i in range(len(a)):
img = paths[i]
image = cv2.imread(img)
image = cv2.resize(image,[224,224])
data.append(image)
In [11]:
data = np.array(data)
data.shape
Out[11]:
(9323, 224, 224, 3)
In [12]:
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
In [13]:
df.ClassName = le.fit_transform(df.ClassName)
In [14]:
x = data
y = df.ClassName
In [15]:
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.1)
Resnet 50¶
In [23]:
import numpy as np
import cv2
import PIL.Image as Image
import os
import matplotlib.pylab as plt
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
from keras.models import Sequential
from keras import layers,models
from keras.layers import Flatten,Dense
from keras.models import Model
In [24]:
IMAGE_SIZE = [224,224]
vgg = VGG16(input_shape=IMAGE_SIZE+[3],weights='imagenet',include_top=False)
In [25]:
for layers in vgg.layers:
layers.trainable = False
In [27]:
x = Flatten()(vgg.output)
prediction = Dense(61,activation = 'softmax')(x)
In [28]:
model2 = Model(inputs = vgg.input, outputs = prediction)
In [29]:
model2.summary()
Model: "model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 224, 224, 3)] 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 _________________________________________________________________ flatten_1 (Flatten) (None, 25088) 0 _________________________________________________________________ dense_2 (Dense) (None, 61) 1530429 ================================================================= Total params: 16,245,117 Trainable params: 1,530,429 Non-trainable params: 14,714,688 _________________________________________________________________
In [30]:
model2.compile(optimizer = 'adam',
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics = ['accuracy'])
In [31]:
model2.fit(x_train,y_train,epochs=10)
Epoch 1/10 263/263 [==============================] - 2912s 11s/step - loss: 53.7563 - accuracy: 0.3485 Epoch 2/10 263/263 [==============================] - 1906s 7s/step - loss: 10.8489 - accuracy: 0.8166 Epoch 3/10 263/263 [==============================] - 1845s 7s/step - loss: 6.1014 - accuracy: 0.8875 Epoch 4/10 263/263 [==============================] - 1715s 7s/step - loss: 5.6337 - accuracy: 0.9063 Epoch 5/10 263/263 [==============================] - 1526s 6s/step - loss: 4.9592 - accuracy: 0.9201 Epoch 6/10 263/263 [==============================] - 1927s 7s/step - loss: 4.6979 - accuracy: 0.9249 Epoch 7/10 263/263 [==============================] - 2027s 8s/step - loss: 3.8754 - accuracy: 0.9398 Epoch 8/10 263/263 [==============================] - 2029s 8s/step - loss: 3.8877 - accuracy: 0.9397 Epoch 9/10 263/263 [==============================] - 37489s 143s/step - loss: 3.6109 - accuracy: 0.9435 Epoch 10/10 263/263 [==============================] - 1717s 7s/step - loss: 3.4109 - accuracy: 0.9536
Out[31]:
<keras.callbacks.History at 0x21cf39209d0>
In [35]:
y_pre = model2.predict(x_test)
y_pred = [np.argmax(i) for i in y_pre]
y_pred[:10]
Out[35]:
[2, 14, 9, 36, 39, 58, 35, 17, 23, 54]
In [34]:
y_test[:10]
Out[34]:
8363 2 4200 14 7214 9 5068 60 2162 12 5977 58 718 31 3837 17 9127 42 6276 15 Name: ClassName, dtype: int32
In [36]:
model2.evaluate(x_test,y_test)
30/30 [==============================] - 214s 7s/step - loss: 140.8611 - accuracy: 0.3859
Out[36]:
[140.86111450195312, 0.3858520984649658]
In [37]:
from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred))
precision recall f1-score support
0 0.60 0.33 0.43 9
1 0.89 0.50 0.64 16
2 0.10 0.50 0.16 6
3 0.67 0.84 0.74 19
4 1.00 0.82 0.90 11
5 0.00 0.00 0.00 8
6 0.33 0.18 0.24 11
7 0.00 0.00 0.00 3
8 0.20 0.17 0.18 6
9 0.53 0.26 0.35 66
10 0.00 0.00 0.00 12
11 0.24 0.55 0.34 38
12 0.00 0.00 0.00 5
13 0.28 0.22 0.25 36
14 0.23 0.43 0.30 30
15 0.00 0.00 0.00 17
16 0.08 0.17 0.11 6
17 0.88 0.64 0.74 11
18 0.67 0.51 0.58 39
19 0.00 0.00 0.00 4
20 0.00 0.00 0.00 2
21 0.50 0.14 0.22 7
22 0.83 0.62 0.71 8
23 0.41 0.32 0.36 28
24 0.33 0.20 0.25 15
25 1.00 0.50 0.67 6
26 0.00 0.00 0.00 6
27 1.00 0.09 0.17 11
28 0.22 0.50 0.31 8
29 0.00 0.00 0.00 4
30 0.20 0.26 0.23 19
31 0.00 0.00 0.00 11
32 0.88 0.78 0.82 9
34 1.00 0.14 0.25 7
35 0.12 0.07 0.09 14
36 0.06 0.10 0.07 21
37 0.00 0.00 0.00 1
38 0.00 0.00 0.00 9
39 0.40 0.80 0.53 10
40 0.00 0.00 0.00 1
41 0.23 0.60 0.33 10
42 0.31 0.25 0.28 16
43 0.50 0.27 0.35 26
44 0.62 0.62 0.62 47
45 0.25 0.25 0.25 4
46 0.00 0.00 0.00 3
47 0.00 0.00 0.00 7
48 0.00 0.00 0.00 7
49 0.38 0.33 0.35 9
50 0.00 0.00 0.00 13
51 0.25 0.18 0.21 17
52 0.00 0.00 0.00 7
53 0.43 0.34 0.38 47
54 0.09 0.22 0.13 9
55 0.58 0.82 0.68 87
56 0.25 0.20 0.22 10
57 0.27 0.33 0.30 9
58 0.89 0.83 0.86 30
59 0.67 0.53 0.59 15
60 0.17 0.10 0.12 10
accuracy 0.39 933
macro avg 0.33 0.28 0.27 933
weighted avg 0.42 0.39 0.38 933
C:\Users\KIIT\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) C:\Users\KIIT\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) C:\Users\KIIT\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
In [52]:
ab = {'water': 55,
'pizza-margherita-baked': 41,
'broccoli': 12,
'salad-leaf-salad-green': 44,
'egg': 23,
'butter': 13,
'bread-white': 9,
'apple': 1,
'dark-chocolate': 22,
'white-coffee-with-caffeine': 57,
'sweet-pepper': 50,
'mixed-salad-chopped-without-sauce': 35,
'tomato-sauce': 54,
'cucumber': 21,
'cheese': 15,
'pasta-spaghetti': 39,
'rice': 43,
'zucchini': 60,
'salmon': 46,
'mixed-vegetables': 36,
'espresso-with-caffeine': 24,
'banana': 3,
'strawberries': 49,
'mayonnaise': 33,
'almonds': 0,
'bread-wholemeal': 11,
'wine-white': 59,
'hard-cheese': 28,
'ham-raw': 27,
'tomato': 53,
'french-beans': 25,
'mandarine': 32,
'wine-red': 58,
'potatoes-steamed': 42,
'croissant': 20,
'carrot': 14,
'salami': 45,
'boisson-au-glucose-50g': 6,
'biscuits': 5,
'corn': 19,
'leaf-spinach': 31,
'tea-green': 52,
'chips-french-fries': 17,
'parmesan': 38,
'beer': 4,
'bread-french-white-flour': 7,
'coffee-with-caffeine': 18,
'chicken': 16,
'soft-cheese': 48,
'tea': 51,
'avocado': 2,
'bread-sourdough': 8,
'gruyere': 26,
'sauce-savoury': 47,
'honey': 29,
'mixed-nuts': 34,
'jam': 30,
'bread-whole-wheat': 10,
'water-mineral': 56,
'onion': 37,
'pickle': 40}
Testing Dataset¶
In [43]:
df_test = pd.read_csv("test.csv")
df_test
Out[43]:
| ImageId | |
|---|---|
| 0 | 90e63a2fde.jpg |
| 1 | a554d1ca8d.jpg |
| 2 | 48317e8ee8.jpg |
| 3 | 79528df667.jpg |
| 4 | 6d2f2f63f5.jpg |
| ... | ... |
| 479 | 843b8695df.jpg |
| 480 | 584490f483.jpg |
| 481 | d5df1ab5ec.jpg |
| 482 | 79e3e01b88.jpg |
| 483 | a05a4fe910.jpg |
484 rows × 1 columns
In [45]:
df_test_name = df_test['ImageId']
df_test_name[0]
Out[45]:
'90e63a2fde.jpg'
In [41]:
img_dir1 = r'C:\Users\KIIT\Documents\AICrowd\test_images'
img_dir1
Out[41]:
'C:\\Users\\KIIT\\Documents\\AICrowd\\test_images'
In [47]:
img = df_test_name[0]
image = cv2.imread(os.path.join(img_dir1, img))
plt.imshow(image)
print(image.shape)
print(img)
(464, 464, 3) 90e63a2fde.jpg
In [51]:
plt.figure(figsize=(8,8))
for i in range(9):
img = df_test_name[i+9]
image = cv2.imread(os.path.join(img_dir1, img))
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.subplot(3,3,i+1)
plt.imshow(image_rgb)
plt.xlabel('Unknown Food')
plt.tight_layout()