Welcome to Eran Feit’s Computer-Vision Hub
Eran Feit
Welcome to Eran Feit’s Computer-Vision Hub
You can follow the world of Computer Vision, TensorFlow, Keras, and Python with my tutorials.
My last Posts:
Build a 100-Class Sports Classifier with EfficientNetB0
This EfficientNetB0 image classification tutorial is designed to teach you how to build a robust system capable of identifying 100 ...
Fast Keras Hub Image Classification Tutorial
Staying ahead in computer vision means moving beyond fragmented libraries and embracing a unified ecosystem. This Keras Hub image classification ...
Classifying Knee X-Rays with ResNet152V2 & TensorFlow
In the field of medical diagnostics, deep learning is no longer just a buzzword—it is a transformative tool that assists ...
How to Train ConvNeXt in PyTorch on a Custom Dataset
ConvNeXt has become one of the most practical “modern CNN” choices when you want strong accuracy without giving up the ...
CNN Image Classification TensorFlow: 30 Musical Instruments
This article is about building a cnn image classification tensorflow project that can recognize 30 different musical instruments from images, ...
Generate synthetic images for image classification in Python
This article explains how to generate synthetic images for image classification using Python, Hugging Face Diffusers, and Stable Diffusion. It ...
Transfer learning using Xception | ship classifier
Xception Transfer Learning Tensorflow is the fastest way to build a strong ship image classifier without training a deep network ...
MediaPipe image classifier Python with EfficientNet-Lite0
Ever wanted a quick way to recognize what’s inside a photo without training a model or building a huge pipeline.This ...
Brain Tumor Segmentation with YOLOv11 in Python
Brain Tumor Segmentation with YOLOv11 in Python: What You’ll Build This article walks through a complete, practical workflow for brain ...
How to UNet Image Segmentation TensorFlow on Custom Data | Dolphin Segmentation
U-Net image segmentation in TensorFlow is a go-to approach when you need pixel-level predictions, not just a single label per ...











