Image Classification using Transfer Learning

Image classification and deep learning are two powerful tools that can help you reach new heights in data science. By combining these two technologies, you can create powerful and accurate models that can solve complex problems. In this blog, we’ll discuss what image classification is, the benefits of image classification, image classification with deep learning, and best practices for image classification.

What is Image Classification

Image classification is a computer vision technique that uses machine learning algorithms to identify and classify objects in an image. It can be used for a wide range of applications, from facial recognition to medical diagnosis. Image classification utilizes a variety of algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), and random forests.

In image classification, images are presented to a computer algorithm, which then performs a series of computations to identify objects in the image. The algorithm then assigns a label or class to each object in the image. For example, an algorithm may be trained to recognize cats, dogs, and birds in a given image.

The process of image classification can be broken down into two parts: feature extraction and classification. Feature extraction is the process of extracting the objects in an image that are relevant to the task at hand. This involves identifying edges, lines, shapes, and other features that can be used to identify an object. The classification process is the process of assigning labels to the extracted features. This can be done manually or with the help of a machine learning algorithm.

Benefits of Image Classification

Image classification has many benefits, including:

Improved accuracy: Image classification algorithms can be more accurate than human annotators, as they are trained to recognize specific features.
Faster processing: Image classification algorithms can process images faster than humans, as they don’t need to manually search for objects in an image.
Cost savings: Automated image classification algorithms can save companies time and money by eliminating the need for manual annotation.
Automation: Image classification algorithms can be used to automate tasks such as facial recognition, object detection, and medical diagnosis.
Scalability: Image classification algorithms can be scaled to process large amounts of data quickly and accurately.

Tensorflow Transfer Learning

TensorFlow is a popular open-source machine learning library that can be used for image classification. It provides a variety of tools and algorithms that can be used to train and deploy image classification models. One of the most powerful tools available in TensorFlow is transfer learning.

Transfer learning is a technique that allows you to take a model that has already been trained on a large dataset and fine-tune it for your own dataset. This can be useful if you don’t have access to a large dataset, or if you want to improve the accuracy of your model. Transfer learning can also save you time, as you don’t have to train a new model from scratch.

Image Classification with Deep Learning Tools

There are a variety of deep learning tools available that can be used for image classification. These tools make it easy to train and deploy image classification models without having to write your own code. Some of the most popular deep learning tools include TensorFlow, Keras, Caffe, and PyTorch.

Keras is another popular deep learning library that can be used for image classification. It provides a high-level API that makes it easy to train and deploy image classification models.

Caffe is a deep learning framework that can be used for image classification. It provides a flexible architecture that makes it easy to deploy and scale image classification models.

PyTorch is a popular deep learning library that can be used for image classification. It provides a library of algorithms and tools that make it easy to train and deploy image classification models.

Demonstration

To start, we will need to install TensorFlow 2 and any other required libraries. You can install TensorFlow by running the following command:


pip install tensorflow

Next, we will need to download and extract a pre-trained model. TensorFlow provides a number of pre-trained models in its TensorFlow Hub library. For this example, we will use the MobileNetV2 model, which has been trained on the ImageNet dataset. You can download and extract the model by running the following code:


import tensorflow as tf
import tensorflow_hub as hub

model = tf.keras.Sequential([
    hub.KerasLayer("https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4",
                   input_shape=(224, 224, 3))
])

Next, we will need to download and prepare our dataset. For this example, we will use the Cats vs Dogs dataset from Kaggle. This dataset contains 25,000 images of cats and dogs, split into training and validation sets.

After downloading and extracting the dataset, we can use the following code to load the images and prepare them for training:

import os
import numpy as np
import matplotlib.pyplot as plt

# Load the images
X = []
Y = []
for filename in os.listdir("train"):
    label = filename.split(".")[0]
    if label == "cat":
        Y.append(0)
    else:
        Y.append(1)
    image = plt.imread("train/" + filename)
    X.append(image)
X = np.array(X)
Y = np.array(Y)

# Split the data into train and validation sets
from sklearn.model_selection import train_test_split
X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.2)

Here's another example!

from keras.applications import VGG16
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Dropout, Flatten
from keras import Model
from keras import optimizers

# Load the VGG16 model
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Add a new classifier layer on top of the base model
x = base_model.output
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(10, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)

# Freeze the base model layers
for layer in base_model.layers:
    layer.trainable = False

# Compile the model
model.compile(optimizer=optimizers.SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model using the 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_generator = train_datagen.flow_from_directory(
        'data/train',
        target_size=(224, 224),
        batch_size=32,
        class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
        'data/val',
        target_size=(224, 224),
        batch_size=32,
        class_mode='categorical')

model.fit_generator(
        train_generator,
        steps_per_epoch=100,
        epochs=10,
        validation_data=validation_generator,
        validation_steps=50)

Tips for Image Classificatoin

There are a few tips that can help you improve the accuracy of your image classification models:

Use data augmentation: Data augmentation is a technique that can be used to increase the amount of data available for training. This can be useful if you don’t have access to a large dataset.
Utilize transfer learning: Transfer learning is a powerful technique that can be used to improve the accuracy of your model.
Use a balanced dataset: It’s important to use a balanced dataset when training your model. This means that the dataset should contain an equal number of samples for each class.
Use a large dataset: The larger the dataset, the better the accuracy of your model.

Best Practices for Image Classification

When it comes to creating accurate image classification models, there are a few best practices that you should follow:

Choose the right algorithm: Not all algorithms are suitable for all tasks. Make sure to choose an algorithm that is suitable for your task.
Use a balanced dataset: As mentioned above, it’s important to use a balanced dataset when training your model.
Utilize transfer learning: Transfer learning can be a powerful tool for improving the accuracy of your model.
Use data augmentation: Data augmentation can be used to increase the amount of data available for training.
Tune your hyperparameters: Hyperparameters are parameters that control the behaviour of your model. Tuning these parameters can help you improve the accuracy of your model.

Challenges of Image Classification

Image classification is a complex task that can be difficult to get right. Some of the challenges that you may face when creating an image classification model include:

Overfitting: Overfitting occurs when the model starts to learn the noise in the data instead of the underlying patterns. This can lead to poor performance on new data.
Unbalanced datasets: Unbalanced datasets can lead to biased models that don’t generalize well.
Poorly labeled data: Poorly labeled data can lead to inaccurate models.
Poorly chosen algorithms: Choosing the wrong algorithm for your task can lead to poor performance.
Lack of data: If you don’t have access to enough data, it can be difficult to train an accurate model.

Conclusion

Image classification and deep learning can be powerful tools for solving complex problems. By combining these two technologies, you can create powerful and accurate models that can identify objects in an image with high accuracy. In this blog, we discussed what image classification is, the benefits of image classification, image classification with deep learning, and best practices for image classification. We also discussed some of the challenges that you may face when creating an image classification model.

If you’re looking to create an accurate image classification model, it’s important to choose the right algorithm, use a balanced dataset, and utilize transfer learning. And remember to use data augmentation to increase the amount of data available for training. With these tips, you can create powerful and accurate image classification models that can solve complex problems.

Image classification and deep learning are two powerful tools that can help you reach new heights in data science. So, what are you waiting for? Start experimenting with image classification and deep learning today and see what you can create!

Suvrakamal Das