How did I train YOLOv12 on a Custom Dataset with GPUs
Train YOLOv12 on a custom dataset using GPU cloud servers. Follow this guide for setup, configuration, and scalable model training.
Adversarial Autoencoders (with Pytorch)
Learn how to build and run an adversarial autoencoder using PyTorch. Solve the problem of unsupervised learning in machine learning.
Object Detection Using Mask R-CNN with TensorFlow 1.14 and Keras (TENSORFLOW-2-0)
Learn how to perform object detection and instance segmentation using Mask R-CNN with TensorFlow 1.14 and Keras.
WGAN: A Guide to Wasserstein Generative Adversarial Networks
Learn about WGAN (Wasserstein Generative Adversarial Networks), how they work, advantages over traditional GANs, and applications in deep learning.
Kolmogorov-Arnold Networks (KAN): Alternative to Multi-Layer Perceptron?
We explore an ML algorithm and examine whether Kolmogorov-Arnold Networks have the potential to replace Multi-layer Perceptrons.
Writing ResNet from Scratch in PyTorch
In this continuation on our series of writing DL models from scratch with PyTorch, we learn how to create, train, and evaluate a ResNet neural network for CIFAR-100 image classification.
AI Reduces Sensory Hallucinations, Even at Night or in Smoke: Inside KAIST’s DNA and MAD Methods
KAIST has published two methods for cutting sensory hallucinations in multimodal AI: the failure where a model misreads what a sensor physically reports, or invents a perception in one channel because another channel suggested it. DNA optimisation teaches vision-language models the physics of thermal, depth and X-ray sensors using their own wrong answers as the training signal. MAD suppresses cross-modal interference at decoding time with no retraining at all. Here is what each method fixes, what the reported numbers do and do not establish, where sensory hallucinations cost the most in production, and what this line of work still leaves unsolved.
SAM 2: Meta’s Next-Gen Model for Video and Image Segmentation
In this article, we will explore SAM 2, which expands the capabilities of the original SAM to handle both images and videos. It excels in real-time object segmentation, enabling dynamic interaction through prompts and memory attention.
FLUX Image Generation with the cloud provider
In this article, we show how to use FLUX image generation models with Paperspace H100s.
Implementing Baidu’s RT-DETR for Real-Time Object Detection
In this tutorial, we explore Baidu’s RT-DETR object detection framework, explaining its implementation, features, and real-time performance. Learn how to use RT-DETR for deep learning, computer vision, and image recognition tasks with step-by-step guidance.