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Introduction TensorFlow, an open-source deep learning library developed by Google, is widely used for building and training machine learning models. When working on real-world problems, you often need to use custom datasets tailored to your specific task. In this blog post, we will guide you through the process of running TensorFlow with a custom dataset, using a simple image classification example. Table of Contents: Understanding Custom Datasets in TensorFlow Preparing the Data Creating a TensorFlow Dataset Building a Convolutional Neural Network (CNN) Model Training the Model Evaluating the Model Conclusion Understanding Custom Datasets in TensorFlow Custom datasets in TensorFlow allow you to work with unique data formats and pre-processing steps essential for your machine learning task. TensorFlow provides a Dataset API that streamlines data loading, batching, and shuffling, making it efficient for training large models with large datasets. Preparing the Data For...

In this tutorial, we'll explore how to use the ImageFolder dataset in PyTorch, a popular deep learning library, to load and preprocess image data for training a neural network. The ImageFolder dataset is useful when dealing with image data organized in a specific folder structure, where each class has its own folder containing images. Dataset Structure Before diving into code examples, let's understand the required folder structure for using ImageFolder : data/ ├── train/ | ├── class_1/ | | ├── image_1.jpg | | └── image_2.jpg | ├── class_2/ | | ├── image_3.jpg | | └── image_4.jpg | └── ... ├── val/ | ├── class_1/ | | ├── image_5.jpg | | └── image_6.jpg | ├── class_2/ | | ├── image_7.jpg | | └── image_8.jpg | └── ... In this example, the images are categorized into classes, and the train and validation sets are organized in separate folders. Code Exampl...