Eran Feit Blog posts

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

Introduction Fine tune vision transformer workflows have become one of the most effective ways to push image classification performance beyond traditional CNNs, especially when working with a custom dataset. Vision Transformers (ViT) bring the power of attention mechanisms from NLP into computer vision, allowing models to understand global image context rather than relying only on

Introduction Vision transformer image classification PyTorch has become one of the most important approaches for solving modern computer vision problems using deep learning. Instead of relying on convolutional layers, Vision Transformers process images as sequences of patches, allowing the model to learn long-range dependencies and global context more effectively. This shift in architecture has opened

Introduction Vision Transformer image classification is changing the way computer vision models understand images by treating them as sequences rather than grids of pixels.Instead of relying on convolutional layers, this approach applies transformer architectures—originally designed for natural language processing—directly to visual data.This shift enables models to capture long-range relationships across an image in a more