ImageSeeker 0.7

Its an auto image classification library

ImageSeeker: An Image Classification Library

This repository hosts the development of the ImageSeeker library.

Authors

• Bappy Ahmed

• Email: [e-mail]

• Github: https://github.com/entbappy/

🚀 About Me

I'm a Data Science learner. This library I have implemented just for learning purpose.

About ImageSeeker

ImageSeeker is a deep learning image classification library written in Python, running on top of the machine learning platform TensorFlow.Keras. It was developed with a focus on enabling fast experimentation of images classification. You can classify any image with any classification model in Keras appliaction without writing any lines of code.

ImageSeeker is:

• Simple -- ImageSeeker reduces developer time of writing too much code of any image classification problem so that they focus on the parts of the problem that really matter.
• Flexible -- Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy that’s why ImageSeeker is Flexible.
• Powerful -- Keras provides industry-strength performance and scalability: so we can use ImageSeeker in the production

Keras & ImageSeeker

Keras is the high-level API of TensorFlow 2: an approachable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning. We can do so many tasks using Keras and image classification is one of them, but the issue is like we need to write many lines of code to implement an image classification solution.

But in ImageSeeker you don’t need to write many lines of code for implementing an image classification solution. You don’t also have to worry about your data preparation. What you need to do is just have to define your data path & some of the parameters of the model yes, your work will be done!

Image Classification using Keras

Necessary library importing

from tensorflow.keras.applications.densenet import VGG16
import tensorflow.keras as keras
from tensorflow.keras import models
from tensorflow.keras import layers
from tensorflow.keras import optimizers
import tensorflow as tf
from keras.models import load_model
import numpy as np
import matplotlib.pyplot as plt
import os

Redirecting to the data path

ROOT = 'H:\\Parsonal\\Coding Practice\\dogCat'
os.chdir(ROOT)
os.getcwd()

Preparing data & applying augmentation

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale= 1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip= True)

test_datagen = ImageDataGenerator(rescale= 1./255)

Train_path = "H:\\Parsonal\\Coding Practice\\dogCat\\train"
Test_path = "H:\\Parsonal\\Coding Practice\\dogCat\\valid"

training_set = train_datagen.flow_from_directory(directory=Train_path,
                                                 target_size=(224,224),
                                                 batch_size=32,
                                                 class_mode='categorical')


test_set = test_datagen.flow_from_directory(directory=Test_path,
                                                 target_size=(224,224),
                                                 batch_size=32,
                                                 class_mode='categorical')

Dowloading models

VGG = tf.keras.applications.VGG16(
    include_top=False,
    weights="imagenet",
    input_shape=(224,224,3)

)

VGG.summary()

Freezing layers

for layer in VGG.layers:
    layer.trainable = False

Adding custom layers

model = models.Sequential()
model.add(VGG)               
model.add(layers.Flatten())
model.add(layers.Dense(128, activation='relu' ))  
model.add(layers.Dropout(0.2))
model.add(layers.Dense(2, activation='softmax')) 
#model.summary()

Defining optimizers and loss function

from tensorflow.keras.optimizers import RMSprop
loss = 'categorical_crossentropy'
optimizer =RMSprop(learning_rate=0.0001)

Defining Tensorboard log and checkpoint

# Log

import time 

def get_log_path(log_dir="logs/fit"):
  fileName = time.strftime("log_%Y_%m_%d_%H_%M_%S")
  logs_path = os.path.join(log_dir, fileName)
  print(f"Saving logs at {logs_path}")
  return logs_path

log_dir = get_log_path()
tb_cb = tf.keras.callbacks.TensorBoard(log_dir=log_dir)


# checkpoint

CKPT_path = "Model_ckpt.h5"
checkpointing_cb = tf.keras.callbacks.ModelCheckpoint(CKPT_path, save_best_only=True)

Compiling model

model.compile(optimizer = optimizer, loss=loss, metrics=['accuracy'])

Start training

hist = model.fit(training_set,
                         steps_per_epoch = 10,
                         epochs = 5,
                         validation_data = test_set,    
                         validation_steps = 20,
                         callbacks=[tb_cb,checkpointing_cb]
                         )

Epoch 1/5 10/10 [==============================] - 231s 21s/step - loss: 3.4007 - accuracy: 0.3812 - val_loss: 0.8920 - val_accuracy: 0.6438 Epoch 2/5

10/10 [==============================] - 100s 10s/step - loss: 1.0077 - accuracy: 0.6375 - val_loss: 0.6927 - val_accuracy: 0.7328 Epoch 3/5

10/10 [==============================] - 83s 9s/step - loss: 0.6629 - accuracy: 0.7344 - val_loss: 0.4763 - val_accuracy: 0.8266 Epoch 4/5

10/10 [==============================] - 69s 7s/step - loss: 0.4356 - accuracy: 0.8406 - val_loss: 0.6980 - val_accuracy: 0.7859 Epoch 5/5

10/10 [==============================] - 64s 6s/step - loss: 0.7226 - accuracy: 0.7844 - val_loss: 0.3199 - val_accuracy: 0.8906

Note: Then again you need write code for prediction as well

Image Classification using ImageSeeker:

>>Installation / Setup of ImageSeeker

First of all create a new conda environment and activate the environment:

 conda create -n imageseeker python==3.7.10 -y

 conda activate imageseeker

Great! After that install ImageSeeker from pypi using that command:

 pip install ImageSeeker

If you done the installation then from your terminal just write imgseeker keyword it will automatically launch the UI on your local host.

Step-1:

Launching UI on local host:

Yes!! You have got 😀 Now if you scroll down, you will see how you can get start with ImageSeeker below

Step-2:

Click on GET STARTED

After clicking on that you will get a form like that and you have to configure it for training

Step-3:

Configure those parameters: Example

Now just hit Start Trainig your training will be started And you can see all the log from your terminal itself like that.

Detected pretrain model!!

Model has been saved following directory : Models\VGG16.h5 Preparing model...

Freezing all...

Adding sigmoid...

Model loaded!!

Augmetation applied!

Found 200 images belonging to 2 classes.

Found 110 images belonging to 2 classes.

Saving logs at Tensorboard/logs/fit\log_2021_09_09_02_24_11 Epoch 1/5 6/6 [==============================] - 35s 6s/step - loss: 6.3008 - accuracy: 0.5385 - val_loss: 1.5761 - val_accuracy: 0.5104 Epoch 2/5

6/6 [==============================] - 29s 5s/step - loss: 2.8433 - accuracy: 0.5109 - val_loss: 2.4668 - val_accuracy: 0.5312 Epoch 3/5

6/6 [==============================] - 29s 5s/step - loss: 2.3428 - accuracy: 0.4800 - val_loss: 0.8409 - val_accuracy: 0.5417 Epoch 4/5

6/6 [==============================] - 29s 5s/step - loss: 0.8796 - accuracy: 0.5927 - val_loss: 0.6251 - val_accuracy: 0.6667 Epoch 5/5

6/6 [==============================] - 29s 5s/step - loss: 0.6290 - accuracy: 0.6746 - val_loss: 0.5840 - val_accuracy: 0.7188 Model saved at the following location : New_trained_model/newVGG16.h5

Step-4:

Prediction:

Awsome!! 😀😀 You have done the training. Now it's time for predicting. You see there is a button called Predict

If you hit that button then you will get your prediction UI like that. Now you can upload images & start predicting😎

See the class label in your terminal

Found 200 images belonging to 2 classes.

Found 110 images belonging to 2 classes.

{'cat': 0, 'dog': 1}

Congratulations !! 😀✌ You are done 😊

Yes! that's how it is simple.

Acknowledgements

• Keras
• SUNNY BHAVEEN CHANDRA
• Sourangshu Pal
• Python