This is a custom single object detection model used to detect a specific object in a given image.

The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out).

T5 Small is a lightweight, 60M-parameter text-to-text transformer, ideal for resource-constrained NLP tasks, offering efficiency and versatility for quick prototyping and deployment.

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based deep learning model developed by Google in 2018

The U-Net is a convolutional neural network designed for image segmentation, featuring a U-shaped architecture. It consists of an encoder (contracting path) to capture context and a decoder (expanding path) for precise localization. Skip connections bridge the encoder and decoder, ensuring spatial information is preserved.

ResNet-34 is a convolutional neural network (CNN) architecture that is part of the ResNet (Residual Network) family, introduced in the groundbreaking 2015 paper "Deep Residual Learning for Image Recognition" .

The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out).