Eran Feit

Eran Feit is a Computer Vision Engineer, AI Researcher, and Deep Learning Educator with over a decade of hands-on experience in the tech industry. Specializing in object detection, edge AI deployment, and advanced neural network architectures, Eran bridges the gap between complex AI theory and practical implementation. He is dedicated to empowering the global developer community through comprehensive, code-driven tutorials and guides.

How to Use Grounding DINO with Segment Anything Tutorial

Image Segmentation, Python Cool Stuff /
grounding dino segment anything tutorial

Introduction In the world of AI-powered computer vision, combining detection, segmentation, and creative editing in a single pipeline is a major breakthrough. The grounding dino segment anything tutorial introduces precisely such a workflow — allowing you to detect arbitrary objects described in text, segment them precisely, and even manipulate them (for example via inpainting or

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Python Subtitle Generator Faster-Whisper: Create SRT Files Guide

Python Cool Stuff /
Subtitle Generator Guide

Introduction Building a Python Subtitle Generator Faster-Whisper is a game-changer for content creators and developers looking for high-speed, accurate transcription. This guide provides a comprehensive workflow to automate speech-to-text conversion using the optimized Faster-Whisper engine. By the end of this tutorial, you will be able to process audio and video files directly into professional SRT

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AI Object Removal Using Python: A Practical Guide

Python Cool Stuff /
AI Object Removal Using Python

Introduction AI Object Removal Using Python has quickly become one of the most practical and exciting applications of modern computer vision. It allows anyone—from developers to digital artists—to automatically erase unwanted objects from images without manually editing pixels. Instead of relying on traditional tools like clone stamping or masking, deep learning models can now detect

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Image Captioning using PyTorch and Transformers in Python

Python Cool Stuff /
Image Captioning using PyTorch and Transformers in Python

Image captioning python is all about teaching a computer to look at a picture and describe it in natural language. Instead of manually writing alt-text or descriptions for every image, you use deep learning models to generate sentences automatically. With a few lines of code in Python, you can load a pre-trained vision–language model, pass

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Object Detection Heatmap for Tracking Moving Dogs

Object Detection, Pytorch /
object detection heatmap

Object detection heatmap is a simple idea with a lot of power behind it.Instead of just drawing bounding boxes around objects, you aggregate all those detections into a colorful map that shows where activity is concentrated.Each new detection slightly “warms up” the corresponding region of the frame, so after processing many frames you get a

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YOLOv8 Stanford Dogs Tutorial: XML to YOLO Labels, Train, and Predict

Object Detection, Pytorch /
YOLOv8 dog detection and training on 120 breeds

Building high-accuracy computer vision models requires more than just code; it requires the right architecture and data synergy. If you are struggling with generic object detection, learning how to train YOLOv8 on the Stanford Dogs dataset is the ultimate way to master fine-grained image classification and detection. In this guide, we solve the challenge of

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YOLOv8 YouTube Object Detection in Python (Auto-Label + Live Inference)

Object Detection, Pytorch /
How to use YOLOv8 for object detection on YouTube videos

YOLOv8 YouTube object detection in Python: a full pipeline you can reuse YOLOv8 YouTube object detection is one of the fastest ways to move from “demo code” to a real computer-vision workflow.Instead of training on random images, you build a dataset from actual video footage that matches what you want the model to learn. In

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Automatic Image Annotation with Autodistill and YOLOv8

Object Detection, Pytorch /
Automatic Image Annotation with Autodistill and YOLOv8

The bottleneck of every computer vision project isn’t the architecture—it’s the data. Manually drawing thousands of bounding boxes is slow, prone to human error, and expensive. In this guide, you will master auto-labeling YOLOv8 datasets with Autodistill, a revolutionary “teacher-student” framework. By leveraging massive foundation models like Grounding DINO to “teach” your compact YOLOv8 model,

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Train YOLO-NAS on Custom Dataset: A Step-by-Step Aquarium AI Guide

Object Detection, Pytorch /
Yolo-Nas

Train YOLO-NAS on custom dataset in Python to achieve state-of-the-art object detection performance without the complexity of manual architecture design. While pre-trained models offer a great starting point, the real power of Neural Architecture Search (NAS) is unlocked when you apply it to specialized data, such as the underwater complexities of the Aquarium dataset. In

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Eran Feit