Training YOLOv8 on Custom Data
This blog post explores YOLOv8, comparing its architectural changes to YOLOv5. We’ll also demonstrate the new model’s Python API functionality by testing its detection capabilities on a Basketball dataset.
AI Summarization: Vision Instruct with HuggingFace on Droplets
Learn to create a powerful AI summarization tool for presentations using 1-click Vision Instruct models in this tutorial with Hugging Face
Automated metrics for evaluating the quality of text generation
Follow this blog post to learn about several of the best metrics used for evaluating the quality of generated text, including: BLEU, ROUGE, BERTscore, METEOR, Self-BLEU, and Word Mover’s Distance.
Boosting Python Scripts With Cython
Python might be one of today’s most popular languages, but it’s definitely not the most efficient. See how Cython can easily boost your Python scripts.
Channel Attention and Squeeze-and-Excitation Networks (SENet)
In this article we will cover one of the most influential attention mechanisms proposed in computer vision: channel attention, as seen in Squeeze-and-Excitation Networks (SENet).
How to Connect Google to OpenClaw
Integrate Google services with OpenClaw on a cloud servers. Set up OAuth, enable Gmail, Calendar, and Drive APIs, and connect securely.
How to Improve Text-to-Image Generation with DeciDiffusion and Latent Diffusion
Understand DeciDiffusion, a powerful and efficient text-to-image diffusion model built with U-Net-NAS architecture. Learn how it reduces computational costs while delivering high-quality AI-generated images.
DiffBIR: High Quality Blind Image Restoration with Generative Diffusion Prior
In this tutorial, we walkthrough the DiffBIR technique for blind image resoration. This Stable Diffusion based technique shows much promise, so follow along this tutorial to launch DiffBIR!
Expert Parallelism: Scaling Mixture-of-Experts Models
Learn how Expert Parallelism boosts Mixture-of-Experts model efficiency and GPU scalability for faster, more optimized large-scale deep learning training.
Finetune Multimodel LLM:IDEFICS 9B using A100
In this article, we will learn how to make predictions using the 4-bit quantized Idefics-9B model and fine-tune it on a specific dataset.
