Machine Learning Models
Model Architecture
A model architecture is the choice of a machine learning algorithm along with the underlying structure or design of the machine learning model. Model architecture consists of layers of interconnected nodes or neurons, where each layer of the model performs a specific function, such as data preprocessing, feature extraction, or prediction.
Popular model architectures include decision trees for smaller datasets, and deep neural networks for larger datasets, including feedforward neural networks, convolutional neural networks, and transformers.
Some popular models and model architectures:
VGG-16 is a convolutional neural network (CNN) architecture that was used to win the 2014 ImageNet competition. It is considered to be one of the excellent vision model architectures till date.
YOLOv3 (You Only Look Once) is a state-of-the-art, real-time object detection system that is extremely fast and accurate. It is a single-stage object detection model that goes straight from image pixels to bounding box coordinates and class probabilities in one pass.
MobileNetV2 is a convolutional neural network that is 53 layers deep. It is a very efficient model and is designed for mobile and embedded vision applications.
Depth Anything is an advanced AI model designed for Monocular Depth Estimation (MDE). It is notable for its ability to provide robust depth estimations from a single image, rather than requiring multiple viewpoints as in traditional stereoscopic methods. Trained on 1.5M labeled images and 62M+ unlabeled images jointly.
Large Language Models (LLMs)
A Large Language Model (LLM) is a type of artificial intelligence (AI) program that can recognize and generate text, among other tasks. LLMs are trained on huge sets of data — hence the name “large.” LLMs are built on machine learning: specifically, a type of neural network called a transformer model.