JEPA Series Part 4: Semantic Segmentation Using I-JEPA
Semantic segmentation using I-JEPA for brain tumor segmentation on the BRISC-2025 dataset with the PyTorch framework.| DebuggerCafe
This blog is part of a series of walkthroughs of Life Science AI models, stemming from this article, which was a collaborative effort between AstraZeneca and AMD. The series delves into what was required in order to run drug discovery related AI workloads on AMD MI300X. The first post in this series, available here, focuses on REINVENT4, a molecular design tool used to generate and optimize candidate molecules. This blog, in particular, looks at SemlaFlow, an efficient 3D molecular generation...| AMD ROCm Blogs
In this blog we explore how to use GSplat, a GPU-optimized Python library for training and rendering 3DGS models, on AMD devices. This tutorial will guide you through training a model of a scene from a set of captured images, which will then allow you to render novel views of the scene. We use a port of the original GSplat code that has been optimized for AMD GPUs. The examples used throughout this blog were trained and rendered using an AMD MI300X GPU.| AMD ROCm Blogs
Gesture and sign recognition is a growing field in computer vision, powering accessibility tools and natural user interfaces. Most beginner projects rely on hand landmarks or small CNNs, but these often miss the bigger picture because gestures are no...| freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
So, in 2025, which should you choose: PyTorch for fast-paced AI experimentation or TensorFlow for rock-solid production - and could the real answer be BOTH? The post TensorFlow vs PyTorch: Which Framework Should You Choose in 2025? appeared first on ShiftMag.| ShiftMag
In this article, we explore the BiRefNet model for high-resolution dichotomous segmentation. Along with discussing the key elements of the paper, we also create a small background removal codebase usign the pretrained model. The post Introduction to BiRefNet appeared first on DebuggerCafe.| DebuggerCafe
Semantic segmentation using I-JEPA for brain tumor segmentation on the BRISC-2025 dataset with the PyTorch framework.| DebuggerCafe
CuTe DSL Basics — From Hello to Tiled Kernels| Chris Choy
Image classification using I-JEPA PyTorch by adding a linear layer on top of the frozen backbone and training for brain tumor classification.| DebuggerCafe
In this article, we use a pretrained I-JEPA model for image similarity. We specifically use the ViT-H I-JEPA trained with 14x14 patches. The post JEPA Series Part 2: Image Similarity with I-JEPA appeared first on DebuggerCafe.| DebuggerCafe
memo.sugyan.com の記事を書いてから、先行事例の調査が足りていなかったなと反省。 Latent Seed の Gaussian noise 間での morphing はあんまりやっている人いないんじゃないかな、と書いたけど、検索してみると普通に居た。 why settle for a few images from #stablediffusion when you can slowly walk your way around the sample space and create hyponotic videos you can't look away from? In th…| すぎゃーんメモ
In this article, we build a simple video summarizer application using Qwen2.5-Omni 3B model with the UI powered by Gradio. The post Video Summarizer Using Qwen2.5-Omni appeared first on DebuggerCafe.| DebuggerCafe
Fine-tuning SmolLM2-135M Instruct model on the WMT14 French-to-English subset for machine translation using a small language model.| DebuggerCafe
As AI systems grow in scale and complexity, traditional monolithic neural networks often fall short in adaptability, interpretability, and efficiency. Enter ...| Rehan Guha -Portfolio & Blog
Just sharing ~100 slides about PyTorch 2 internals focusing on recent innovations (Dynamo, Inductor, and ExecuTorch). I had a lot of fun preparing this and The post PyTorch 2 Internals – Talk first appeared on Terra Incognita.| Terra Incognita
In this post, we will take a look at Flow models, which I’ve been obsessed with while reading papers like Glow-TTS and VITS. This post is heavily based on this lecture video by Pieter Abbeel, as well as the accompanied problem sets for the course, available here.| Jake Tae
In this post, we will take a look at relative positional encoding, as introduced in Shaw et al (2018) and refined by Huang et al (2018). This is a topic I meant to explore earlier, but only recently was I able to really force myself to dive into this concept as I started reading about music generation with NLP language models. This is a separate topic for another post of its own, so let’s not get distracted.| Jake Tae
Advanced shape restrictions, such as combinations of monotonicity and convexity / concavity using polyhedral cones| Alex Shtoff
Fitting shape-restricted functions with ease using PyTorch.| Alex Shtoff
We develop an efficient alternative to PyTorch built-in dataloader class for the case of in-memory datasets, and lightweight models.| Alex Shtoff
We demonstrate how we can reduce model size by pruning un-needed neurons.| Alex Shtoff
Guest post by Nat Jeffries, Founding Engineer at Useful Sensors. At Useful Sensors we love using disposable frameworks to deploy on-device transformers. Having built several such frameworks, I real…| Pete Warden's blog
AMD's GPU training optimizations deliver peak performance for advanced AI models through ROCm software stack.| ROCm Blogs
Learn how to optimize large language model inference using vLLM on AMD's MI300X GPUs for enhanced performance and efficiency.| ROCm Blogs
Managing thousands of fleets of Edge devices is not an easy task. At balena we simplify fleet management so our customers can focus on their business.| balena Blog
brought to you by the ITS Research team at QMUL| blog.hpc.qmul.ac.uk
Triton provides an elegant solution to program GPU kernels in Python, positioning itself as a critical component in the modern AI software stack. To deliver performance and portability, it leverages a compiler, the capability of which determines the potential. Hacking the compiler internals is not a simple task. Here are some tips hopefully useful to folks. I’ll try to keep this blog post updated periodically.| Lei.Chat()
I implemented an Online Handwritten Text Recognition (HTR) system using PyTorch, based on Google paper.| Blog
How to get channel-speicific mean and std in PyTorch| Nikita Kozodoi
A Short Guide to PyTorch DDP In this blog post, we explore whattorchrun andDistributedDataParallelare and how they can be used to speed up your neural network training by usingmultiple GPUs. Neural networks, or even deep neural networks, are popular models for machinelearning. Mathematically, they can be interpreted as nested functions withmillions of parameters. If the parameters are tuned well, they can be used tomake predictions, such as when given a photo, it predicts what that photoconta...| QMUL ITS Research Blog
In this post, I’ll perform a small comparative study between the background architecture of TensorFlow: A System for Large-Scale Machine Learning and PyTorch: An Imperative Style, High-Performance Deep Learning Library The information mentioned below is extracted for these two papers. I chose TensorFlow and PyTorch to perform a comparative study as I have used bothContinue reading "Comparative Case Study of ML Systems: Tensorflow vs PyTorch"| Aditya Rohilla
Learn how to train a computer vision algorithm using Pytorch| DareData Blog
Fixing the pytorch unknown CUDA error.| The Cloistered Monkey
Implementing discriminative learning rate across model layers| Nikita Kozodoi
Simple way to extract activations from deep networks with hooks| Nikita Kozodoi
Andrej KarpathyのNeural Networks: Zero to Hero動画シリーズがとても良かったので紹介します。 はじめに 前提 Neural Networks: Zero to Hero 1. ニューラルネットワークと誤差逆伝播法への入門: microgradの構築 2. 言語モデリングへの入門: makemoreの構築 3. makemoreの構築その２: MLP 4. makemoreの構築その３: 活性化と勾配、バッチ正規化 5. makemoreの構築その４: 誤差逆伝播の達人へ 6. makemoreの構築...| BioErrorLog Tech Blog
torch.tensor()とtorch.Tensor()の違いについての備忘録です。 はじめに torch.tensorとtorch.Tensorの違い 一言で 詳しく 補足: 空のtensorを作るには おわりに 参考 はじめに PyTorchでtensorを作るときはtorch.tensor()メソッドが使われることが多いですね。 一方でtorch.Tensor()のようにクラスのコンストラクタをそのまま呼び出してもtensorを作れるように見えます。 これらふたつ、 torch.tensor() ...| BioErrorLog Tech Blog
In this post of the PyTorch Introduction, we’ll learn how to use custom datasets with PyTorch, particularly tabular, vision and text data| DareData Blog
Cet article est un guide à l’attention des débutants en Intelligence Artificielle qui souhaite commencer à apprendre le Deep Learning. Comment ne pas avoir entendu parler de l’Intelligence Artificielle à notre époque? De ChatGPT, en passant par AlphaFold et les voitures autonomes, pour arriver à Midjourney, il est aujourd’hui clair que l’IA n’est plus de […] L’article Commencer le Deep Learning en 2024 – Meilleur Guide Simple est apparu en premier sur Inside Machine Lear...| Inside Machine Learning
RT-DETR is a Real-Time Detection Transformer model with state-of-the-art performance and speed on image and video inference using PyTorch.| DebuggerCafe
Continuing the Pytorch series, in this post we’ll learn about how non-linearities help solve complex problems in the context of neural networks| DareData Blog
The YOLO (You Only Look Once) series of models, renowned for its real-time object detection capabilities, owes much of its effectiveness to its specialized loss functions. In this article, we delve into the various YOLO loss function integral to YOLO's evolution, focusing on their implementation in PyTorch. Our aim is to provide a clear, technical| LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
Generate snowflakes using neural cellular automata| jerpint
Learn how to build your first PyTorch model, by using the “magical” Linear layer| DareData Blog
In my last blog, I compared the purposes and design philosophies of Taichi Lang and PyTorch. Now, it's time to take a closer look at their data containers - the most essential part of any easy-to-use programming language.| docs.taichi-lang.org
"How does Taichi differ from PyTorch? They are both embedded in Python and can run on GPU! And when should I choose Taichi over PyTorch or the other way around?"| docs.taichi-lang.org
Learn about Tensors and how to use them in one of the most famous machine learning libraries, PyTorch| DareData Blog
In training workloads, there may occur some scenarios in which graph re-compilations occur. This can create system latency and slow down the overall training process with multiple iterations of graph compilation.| Intel Gaudi Developers
In this post, we show you how to run Habana’s DeepSpeed enabled BERT1.5B model from our Model-References repository.| Habana Developers
In this post, we will learn how to run PyTorch stable diffusion inference on Habana Gaudi processor, expressly designed for the purpose of efficiently accelerating AI Deep Learning models.| Habana Developers
In this article, we will work with a vision transformer from PyTorch’s Torchvision library, providing simple code examples that you can execute on your own machine without the need to download and install numerous code and dataset dependencies. The self-contained baseline training script comprises approximately 100 lines of code, excluding whitespace and code comments.... Read more »| Lightning AI
Learn how to get started with Pytorch 2.0 and Hugging Face Transformers and reduce your training time up to 2x.| www.philschmid.de
