Walkthrough

— that’s the ambitious title the authors selected for his or her paper introducing each YOLOv2 and YOLO9000. The title of the paper itself is “” , which was published back in December 2016. The...

In my previous article I explained how YOLOv1 works and tips on how to construct the architecture from scratch with PyTorch. In today’s article, I'm going to deal with the loss function used to...

If we speak about object detection, one model that likely involves our mind first is YOLO — well, at the least for me, because of its popularity in the sector of computer vision. The very first version...

Welcome back to the Tiny Giant series — a series where I share what I learned about MobileNet architectures. Up to now two articles I covered MobileNetV1 and MobileNetV2. Take a look at references ...

Introduction was a breakthrough in the sphere of computer vision because it proved that deep learning models don't necessarily should be computationally expensive to realize high accuracy. Last month I posted an article where...

Because the title suggests, in this text I'm going to implement the Transformer architecture from scratch with PyTorch — yes, literally from scratch. Before we get into it, let me provide a temporary overview...

From LLaVA, Flamingo, to NVLMMulti-modal LLM development has been advancing fast lately.Although the industrial multi-modal models like GPT-4v, GPT-4o, Gemini, and Claude 3.5 Sonnet are probably the most eye-catching performers today, the open-source models...

Meta’s open-source Seamless models: A deep dive into translation model architectures and a Python implementation guide using HuggingFaceProceed reading on Towards Data Science »