This repository hosts the development of the ImageSeeker library.
Email: [email protected]
Github: https://github.com/entbappy/
I'm a Data Science learner. This library I have implemented just for learning purpose.
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 by just writing 4 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 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!
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 = FalseAdding 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
Define some parameters in the configuration.py
from tensorflow.keras.applications import VGG16
# Configure your data
TRAIN_DATA_DIR = "H:\\Parsonal\\Coding Practice\\iNeuron\\Moduler Coding\\ImageSeeker\\data\\train" # Your training data path
VALID_DATA_DIR = "H:\\Parsonal\\Coding Practice\\iNeuron\\Moduler Coding\\ImageSeeker\\data\\valid" # Your validation data path
CLASSES = 2 # Number of classes in your data
IMAGE_SIZE = (224,224,3) #Image resulution/dimention with respect to your classification models
AUGMENTATION = True # If you want to apply Augmentation in your data (Default is True)
BATCH_SIZE = 32 # Number of batch (Default is 32)
PREDICTION_DATA_DIR = 'H:\\Parsonal\\\Coding Practice\\iNeuron\\Moduler Coding\\ImageSeeker\\prediction' # Your prediction/test data path
# Configure your model
MODEL_OBJ = ResNet50(include_top=False,weights="imagenet",input_shape=(224,224,3)) # Your pretrain model object
PRETRAIN_MODEL_DIR = None #If you have any pretrain model exist path (Default is None)
MODEL_NAME ='ResNet50' # Your model name
EPOCHS = 5 # Number of Epochs
OPTIMIZER = 'adam' # Optimizers name/object
LOSS_FUNC = 'categorical_crossentropy' # Your loss function name/object
FREEZE_ALL= True # Model layers freezing (Default is True)
FREEZE_TILL=None # You can define number of freezing layers (Defualt is None)
Great! Now open your Notebook
# Import some Modules of ImageSeeker
import os
from utils import config
from utils import data_manager as dm
import train_engine
import predict_engineNow reload the configuration you made change
config.configureData()
config.configureModel()Start training
train_engine.train()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
See the classes
dm.class_name()Augmetation applied!
Found 200 images belonging to 2 classes.
Found 110 images belonging to 2 classes.
{'cat': 0, 'dog': 1}
predict_engine.predict()Original image : cat.2012.jpg. Predicted as [0]
Original image : cat.2018.jpg. Predicted as [0]
Original image : dog.2001.jpg. Predicted as [1]
Original image : dog.2002.jpg. Predicted as [1]
%load_ext tensorboard
%tensorboard --logdir Tensorboard/logs/fitYou have to just clone ImageSeeker and install reqiurements.txt
git clone https://github.com/entbappy/ImageSeeker-An-Image-Classification-Library.gitNow locate your terminal to ImageSeeker directory
pip install -r reqiurements.txtAfter cloning the folder structure should look like that...
├── [data] ## This folder contains your train & valid data
│ └────[train]
│ │ ├── [cat]
│ │ └── [dog]
└────[valid]
├── [cat]
├── [dog]
├── [utils]
│ ├── __init__.py
│ ├── callbacks.py
│ └── config.py
├── data_manager.py
│ └── model.py
├── [Checkpoint] ## Training checkpoints will automatically create here
│ │
│ └── [models]
│ ├── VGG16.h5
│ └── Resnet50.h5
|── [Models] ## Pretrain model will be saved here
|── [New_trained_model] ## Training model will be saved here
|── [Prediction]
├── dog.jpg ## testing image for prediction
│ └── cat.jpeg ## testing image for prediction
├── [Tensorboard] ## Tensorboard log will be generated here
├── Tutorial.ipynb
├── configuration.py
├── train_engine.py
|── predict_engine.py
├── README.md
├── requirements.txt