Eran Feit Blog posts

🧠 Introduction — FasterViT Image Classification Using Custom Dataset FasterViT image classification using custom dataset represents a modern, efficient approach to training deep learning models that can recognize and categorize images from your own tailored collection of visual data. In a world where off-the-shelf datasets often don’t match specific application needs, applying models like FasterViT

Introduction — fastervit image classification tutorial A fastervit image classification tutorial introduces a powerful and efficient way to recognize visual patterns in images using modern deep learning techniques. FasterViT is a hybrid model that combines the strengths of convolutional neural networks (CNNs) with vision transformers to deliver both high accuracy and fast processing. For developers

Introduction The term fine tune ConvNeXT refers to the process of adapting a powerful, pre-trained ConvNeXt model to excel at a specific task such as classifying dog breeds in your custom dataset. ConvNeXt itself is a modern convolutional neural network architecture that reimagines classic CNN designs using insights from Vision Transformers, giving it strong performance

Introduction ConvNeXt image classification is a powerful approach for teaching computers to recognize what appears inside images by using a modern deep-learning architecture. Instead of relying on hand-crafted rules, the model learns directly from large datasets and discovers the visual patterns that define objects, scenes, or categories. This makes ConvNeXt a flexible and accurate foundation

Introduction Audio classification with transformers has become one of the most effective ways to understand and analyze sound using modern deep learning. Instead of relying on handcrafted audio features or traditional signal-processing pipelines, transformer-based models learn rich audio representations directly from raw waveforms. This approach allows models to capture both short-term acoustic patterns and longer

Why Fine-tuning Vision Transformer (ViT) Is Better Than Training From Scratch To achieve state-of-the-art results in modern image classification, learning how to fine-tune Vision Transformer on custom dataset is a critical skill for any AI developer. While pre-trained models are powerful, specializing them for your specific data is what drives real-world performance. In this tutorial,