YOLO v9 is the latest iteration in the YOLO series, known for its significant advancements in real-time object detection, efficiency, and accuracy.

The Convolutional Vision Transformer (CvT) integrates convolutional operations into the transformer architecture, enhancing performance and efficiency in image processing.

OWLv2 is an advanced machine learning model developed by Google, designed to improve zero-shot object detection.

LLaVa is a state-of-the-art machine learning model designed for advanced multimodal language and vision understanding.

The Depth Anything model is a foundation model for robust monocular depth estimation

LLaMA 2 7B model is an auto-regressive language model developed by Meta, designed for a variety of natural language processing tasks.

SeamlessM4T v2 is a multimodal machine translation model developed by Meta AI, capable of translating speech and text across multiple languages.

Detectron2 is a cutting-edge machine learning model from Meta AI, renowned for its high performance in object detection and segmentation tasks across diverse fields.

OpenCV offers a range of machine learning models and algorithms for various applications in computer vision.

Grounding DINO v2 enhances open-set object detection capabilities by integrating DINO's self-supervised learning with grounded pre-training, catering to the detection of both known and unknown objects.

OpenAI Whisper v3 is a cutting-edge speech recognition model offering enhanced accuracy, multilingual support, and advanced features for efficient speech-to-text conversion.

AdaBoost is an ensemble ML algorithm that enhances the performance of weak classifiers to create a strong predictive model.

ResNet-50 is a deep learning model known for its novel residual learning framework, significantly impacting computer vision and other domains.

DistilBERT is a streamlined version of the BERT model, offering a balance between performance and efficiency for various natural language processing tasks.

YOLO v8 is a fast and accurate deep learning model for real-time object detection in images and videos.

CatBoost is an open-source gradient boosting library from Yandex, known for efficient handling of categorical data.

LightGBM is a fast, distributed, high-performance gradient boosting framework, used in many winning machine learning competitions.

The Segment Anything Model (SAM) is an advanced machine learning model designed for highly versatile and precise image segmentation across diverse scenarios.

XGBoost is an optimized distributed gradient boosting library designed for efficiency, flexibility, and portability.