We introduce the best lightweight computer vision models for fast production, accurate detection, and ease of use.

U-Net is an image segmentation model that features a U-shaped architecture, comprising two main parts: an encoder and decoder.

RetinaNet is a single-stage object detector that uses Focal Loss and two task-specific sub-networks for object detection.

Explore the decision tree algorithm and enhance your Python skills with step-by-step instructions in this comprehensive guide.

Object and image localization in computer vision: enabling machines to detect and accurately pinpoint objects in images.

Model drift can degrade a model's performance. Learn about the differences between concept drift vs data drift and mitigation strategies.

Gradient descent is based on a gradual, iterative approach to solving the problem of a function's minimization.

Knowledge Graph is a knowledge base that uses graph data structure to store and operate on the data. It powers applications such as Google

MLflow is an open-source platform that helps streamline the ML process and helps solve incurred challenges with model experimentation.

Learn the ins and outs of Kaggle, including finding useful datasets for ML projects and partaking in competitions.

Batch normalization, CNN, neural network, network layers, deep learning method, gradient descent, convolutional neural networks

Grounded-SAM offers the best of both worlds in image segmentation, combining Grounding DINO with Segmentation Anything.