The following repository hosts the soda-net modules which is a binary classifier for distinguishing whether a network contains soda bottles (in particular pepsi or coca-cola based products) over others.
Model: "sequential"
| Layer (type) | Output Shape | Parameters |
|---|---|---|
| conv2d (Conv2D) | (None, 78, 78, 48) | 3648 |
| max_pooling2d (MaxPooling2D) | (None, 39, 39, 48) | 0 |
| conv2d_1 (Conv2D) | (None, 20, 20, 96) | 115296 |
| max_pooling2d_1 (MaxPooling2) | (None, 10, 10, 96) | 0 |
| batch_normalization | (None, 10, 10, 96) | 384 |
| conv2d_2 (Conv2D) | (None, 5, 5, 192) | 460992 |
| batch_normalization_1 | (None, 5, 5, 192) | 768 |
| conv2d_3 (Conv2D) | (None, 3, 3, 192) | 921792 |
| max_pooling2d_2 | (None, 1, 1, 192) | 0 |
| batch_normalization_2 | (None, 1, 1, 192) | 768 |
| conv2d_4 (Conv2D) | (None, 1, 1, 256) | 1229056 |
| batch_normalization_3 | (None, 1, 1, 256) | 1024 |
| flatten (Flatten) | (None, 256) | 0 |
| dense (Dense) | (None, 512) | 131584 |
| dense_1 (Dense) | (None, 256) | 131328 |
| dense_2 (Dense) | (None, 128) | 32896 |
| dense_3 (Dense) | (None, 2) | 258 |
Total params: 3,029,794 Trainable params: 3,028,322 Non-trainable params: 1,472
| Method Name | #Inputs | Description |
|---|---|---|
| get_model | None | Returns a copy of the model. |
| set_model | Pytorch Model | Reinsantiates a new temporary model (optional, and usage limited to advanced exploration) |
| predict | None | Returns the predicted score (between 0 and 1) for a particular image or set of images. |
| evaluate | csv file path (optional) | Returns a binary prediction alongside saving the results in the csv file if the csv path was set. |
| retrain | folder path for positive, negative images for training | Retrains the network on user-defined images for a more hybrid and customized model |
| load_image | image variable (Preferable: numpy/cv2/Image) | Serves image to the network for making predictions |
| load_image_from_file | folder path containing image | Loads image from the folder to be served to the network for making predictions |
# Required packages (general)
from sodanet_model import SodaModel
from matplotlib.image import imread
from utilities import compute_accuracy_labelwise
# Demo-specific packages (not required for the working of this module)
from matplotlib import pyplot as plt
import pandas as pd# Prediction from single image variable
image_path = 'dataset/custom/temp_coke/5a86e60cd0307215038b4797.jpg'
im = imread(image_path)
plt.imshow(im)
plt.show()
# Loading the sodanet module
model = SodaModel()# Making predictions for a single image variable
model.load_image(im)
predicted, im_ret = model.evaluate()
print ("Predicted: 0-Not Coke, 1-Coke : ", predicted) # 0: Not Coke, 1: Coke
plt.imshow(im_ret)
plt.show()Predicted: 0-Not Coke, 1-Coke : [1]
# Resizing single image file
import cv2
import os
import numpy as np
def resize_image_single(im, output_shape=160):
resized = cv2.resize(im, (output_shape, output_shape), interpolation=cv2.INTER_AREA)
resized[resized<0] = 0
if resized.shape == (output_shape, output_shape, 4):
resized = resized[:, :, :3]
return cv2.normalize(resized, None, alpha = 0, beta = 255, norm_type = cv2.NORM_MINMAX,
dtype = cv2.CV_32F).astype(np.uint8)# Prediction from multiple image variable derived from a directory
max_files = 10
base_path = 'dataset/custom/temp_coke/'
files = os.listdir(base_path)[:max_files]
im = np.array([resize_image_single(imread(os.path.join(base_path, file))) for file in files])
model.load_image(im)
predicted, ret_im = model.evaluate()
print (predicted)
print ("Is returned img array same as the original ? : {}".format(np.testing.assert_almost_equal(im, ret_im) is None))[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
Is returned img array same as the original ? : True
# Prediction for a batch of files plus as well as displaying what goes inside the csv file
model.load_image_from_file('dataset/custom/temp_not_coke')
_, results_tray = model.evaluate(output_csv_path='dataset/output/not_coke_pred.csv', mode='w')
print ("Rightly-classified Accuracy = {}".format(compute_accuracy_labelwise(results_tray, 1)))
display(pd.read_csv('dataset/output/not_coke_pred.csv'))Rightly-classified Accuracy = 0.7236842105263158
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| 2997084866_a6f8749434_o.jpg | 0 | |
|---|---|---|
| 0 | 3023311841_c26dac5dfd_o.jpg | 0 |
| 1 | 3023311851_6f933bfb3a_o.jpg | 0 |
| 2 | 3023311879_a4e284202b_o.jpg | 0 |
| 3 | 3024147898_082442f05a_o.jpg | 0 |
| 4 | 3025918647_703a4de870_o.jpg | 0 |
| ... | ... | ... |
| 70 | 3593355893_a52329d7f7_o.jpg | 1 |
| 71 | 3593912700_e5704573b3_b.jpg | 0 |
| 72 | 3609363287_2a698128fe_o.jpg | 0 |
| 73 | 3617371187_4db81e2829_o.jpg | 0 |
| 74 | cutest-puppy-dog-pictures-coverimage.jpg | 0 |
75 rows × 2 columns
model.load_image_from_file('dataset/custom/temp_coke')
_, results_tray = model.evaluate(output_csv_path='dataset/output/coke_pred.csv', mode='w')
print ("Rightly-classified Accuracy = {}".format(compute_accuracy_labelwise(results_tray, 0)))
display(pd.read_csv('dataset/output/coke_pred.csv'))Rightly-classified Accuracy = 1.0
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| 0.jpg | 1 | |
|---|---|---|
| 0 | 1030426_d94dfc35f3_o.jpg | 1 |
| 1 | 109941551_962c4dcdea_b.jpg | 1 |
| 2 | 13294174925_d34609da50_o.jpg | 1 |
| 3 | 138554565_77e5ae971c_b.jpg | 1 |
| 4 | 142828186_ab849fa6b8_o.jpg | 1 |
| ... | ... | ... |
| 58 | 960x0.jpg | 1 |
| 59 | 9864241_ac1b13b949_b.jpg | 1 |
| 60 | Coca-cola_1040.jpg | 1 |
| 61 | coke-corona-virus-today-main-200226-v2_1e37aa5... | 1 |
| 62 | Coke_PageProperties.jpg | 1 |
63 rows × 2 columns
# Attempting to increase accuracy through training
dir_coke = 'dataset/custom/temp_coke'
dir_not_coke = 'dataset/custom/temp_not_coke'
validation_coke, validation_not_coke = '', ''
default_transformations_coke, default_transformations_not_coke = ['perform_random_affine_transform', 'flip_rotate',
'add_gaussian_noise'], []
model_params = {'max_epochs': 15, 'external_model_path': 'sodanet/additional_model'}
model.retrain(dir_coke, dir_not_coke, validation_coke, validation_not_coke, default_transformations_coke,
default_transformations_not_coke, model_params)Train on 1320 samples
Epoch 1/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1753 - accuracy: 0.9939
Epoch 2/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1713 - accuracy: 0.9947
Epoch 3/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1669 - accuracy: 0.9970
Epoch 4/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1648 - accuracy: 0.9977
Epoch 5/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1639 - accuracy: 0.9985
Epoch 6/15
1320/1320 [==============================] - 20s 15ms/sample - loss: 0.1627 - accuracy: 0.9992
Epoch 7/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1615 - accuracy: 0.9992
Epoch 8/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1607 - accuracy: 0.9992
Epoch 9/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1600 - accuracy: 0.9992
Epoch 10/15
1320/1320 [==============================] - 20s 16ms/sample - loss: 0.1598 - accuracy: 1.0000
Epoch 11/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1600 - accuracy: 1.0000
Epoch 12/15
1320/1320 [==============================] - 22s 16ms/sample - loss: 0.1601 - accuracy: 1.0000
Epoch 13/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1601 - accuracy: 1.0000
Epoch 14/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1601 - accuracy: 1.0000
Epoch 15/15
1320/1320 [==============================] - 21s 16ms/sample - loss: 0.1601 - accuracy: 1.0000
# Prediction for a batch of files
model.load_image_from_file('dataset/custom/temp_not_coke')
_, results_tray = model.evaluate(output_csv_path='dataset/output/not_coke_pred.csv', mode='w')
print ("Rightly-classified Accuracy = {}".format(compute_accuracy_labelwise(results_tray, 1)))
print (results_tray)
display(pd.read_csv('dataset/output/not_coke_pred.csv'))Rightly-classified Accuracy = 0.8026315789473685 (Improvement of over 8%)!!
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| 2997084866_a6f8749434_o.jpg | 1 | |
|---|---|---|
| 0 | 3023311841_c26dac5dfd_o.jpg | 1 |
| 1 | 3023311851_6f933bfb3a_o.jpg | 1 |
| 2 | 3023311879_a4e284202b_o.jpg | 1 |
| 3 | 3024147898_082442f05a_o.jpg | 1 |
| 4 | 3025918647_703a4de870_o.jpg | 1 |
| ... | ... | ... |
| 70 | 3593355893_a52329d7f7_o.jpg | 1 |
| 71 | 3593912700_e5704573b3_b.jpg | 1 |
| 72 | 3609363287_2a698128fe_o.jpg | 1 |
| 73 | 3617371187_4db81e2829_o.jpg | 1 |
| 74 | cutest-puppy-dog-pictures-coverimage.jpg | 1 |
75 rows × 2 columns
model.load_image_from_file('dataset/custom/temp_coke')
_, results_tray = model.evaluate(output_csv_path='dataset/output/coke_pred.csv', mode='w')
print ("Rightly-classified Accuracy = {}".format(compute_accuracy_labelwise(results_tray, 0)))
print (results_tray)
display(pd.read_csv('dataset/output/coke_pred.csv'))Rightly-classified Accuracy = 1.0 (Recall remains 100% while we still get improvement in F1-Score)
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| 0.jpg | 1 | |
|---|---|---|
| 0 | 1030426_d94dfc35f3_o.jpg | 1 |
| 1 | 109941551_962c4dcdea_b.jpg | 1 |
| 2 | 13294174925_d34609da50_o.jpg | 1 |
| 3 | 138554565_77e5ae971c_b.jpg | 1 |
| 4 | 142828186_ab849fa6b8_o.jpg | 1 |
| ... | ... | ... |
| 58 | 960x0.jpg | 1 |
| 59 | 9864241_ac1b13b949_b.jpg | 1 |
| 60 | Coca-cola_1040.jpg | 1 |
| 61 | coke-corona-virus-today-main-200226-v2_1e37aa5... | 1 |
| 62 | Coke_PageProperties.jpg | 1 |
63 rows × 2 columns