Hands On Guide To Image Classification Using Scikit Learn Keras And Tensorflow

Are you interested in the fascinating world of image classification? Do you want to learn how to utilize popular libraries like Scikit Learn, Keras, and Tensorflow to build robust image classification models? Look no further! In this hands-on guide, we will walk you through the steps of image classification using these powerful tools.

Understanding Image Classification

Image classification is the process of teaching a computer model to recognize and categorize images based on their visual features. This technology has numerous applications, including facial recognition, object detection, and medical imaging analysis.

There are several steps involved in creating an image classification model:

Hands-On Guide To IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI

by Sophy Henn (Kindle Edition)

5 out of 5

Language	:	English
File size	:	11359 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	205 pages
Screen Reader	:	Supported
Mass Market Paperback	:	138 pages
Item Weight	:	5.1 ounces
Dimensions	:	5 x 0.32 x 8 inches

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• Data collection and preprocessing
• Feature extraction
• Model training
• Model evaluation and finetuning
• Prediction

Data Collection and Preprocessing

The first step towards building an image classification model is to collect a dataset containing images of different categories that you want your model to classify. This dataset needs to be properly preprocessed to ensure optimal training performance.

The preprocessing steps may include:

• Resizing images to a consistent resolution
• Cropping images to remove irrelevant backgrounds
• Converting images to grayscale or RGB format
• Normalizing pixel values

Once the dataset is preprocessed, it is crucial to split it into training and testing sets to assess the performance of our model accurately.

Feature Extraction

Feature extraction involves transforming raw image data into a format that can be understood by our model. One popular technique is to use pre-trained convolutional neural networks (CNNs) such as VGG16 or ResNet50.

These pre-trained models, trained on large-scale image datasets, have already learned to extract meaningful features from images. By utilizing the pre-trained models' convolutional layers, we can extract image features, allowing our model to focus on learning the classification task rather than low-level feature extraction.

Model Training

With the extracted features, we can now train our own model using a classifier such as support vector machines (SVM), random forests, or fully connected neural networks.

In this guide, we will focus on using Scikit Learn, Keras, and Tensorflow to build a classification model. These libraries provide user-friendly APIs and efficient algorithms to train models on large datasets.

Model Evaluation and Finetuning

Once the model is trained, we need to evaluate its performance on the testing set. Metrics such as accuracy, precision, recall, and F1 score can be used to assess the model's performance.

If the model does not perform well, we can fine-tune it by adjusting hyperparameters, increasing the training dataset size, or utilizing advanced techniques such as data augmentation or transfer learning.

Prediction

With the final model trained and evaluated, we can now use it for real-world image classification tasks. By feeding the model new images, it will predict the corresponding categories with a certain level of confidence.

Image classification is an exciting field with endless possibilities. By using Scikit Learn, Keras, and Tensorflow, you can explore the world of image classification and build powerful models capable of accurately categorizing images.

Remember to always collect and preprocess your data carefully, utilize pre-trained models for feature extraction, train your own model using powerful algorithms, evaluate its performance, and finetune if necessary.

Now, are you ready to embark on your image classification journey? Follow this hands-on guide, and you'll be well on your way to becoming an image classification expert.

Hands-On Guide To IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI

by Sophy Henn (Kindle Edition)

5 out of 5

Language	:	English
File size	:	11359 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	205 pages
Screen Reader	:	Supported
Mass Market Paperback	:	138 pages
Item Weight	:	5.1 ounces
Dimensions	:	5 x 0.32 x 8 inches

FREE

In this book, implement deep learning-based image classification on detecting face mask, classifying weather, and recognizing flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries.

In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle (https://www.kaggle.com/omkargurav/face-mask-dataset/download).

In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle (https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download).

In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize flower using Flowers Recognition dataset provided by Kaggle (https://www.kaggle.com/alxmamaev/flowers-recognition/download).