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Fast-YOLOv4-SmallObj

The Fast-YOLOv4-SmallObj model is a modified version of Fast-YOLOv4, which is an algorithm used for object detection. The model is designed to improve the detection of small objects, which can be challenging for algorithms to detect accurately. By adding seven layers and predicting bounding boxes at three different scales, the Fast-YOLOv4-SmallObj model improves its accuracy in detecting small objects. Object Detection Object detection is an essential task in computer vision that involves ide

Faster R-CNN

Faster R-CNN: An Improved Object Detection Model If you’re interested in object detection models, then you might have heard about Faster R-CNN. Faster R-CNN is an object detection model, which is an algorithm that analyzes an image or a video and identifies objects in the scene. Object detection models are incredibly useful for many things, such as self-driving cars, image search engines, face recognition, and more. Faster R-CNN improves upon previous models, such as Fast R-CNN, by using a reg

Fastformer

What is Fastformer? Fastformer is a new type of Transformer, a type of neural network commonly used in natural language processing tasks like language translation and text classification. Transformers typically model the pairwise interactions between tokens, or individual units of text, to understand their relationships within a larger context. However, Fastformer uses a different approach called additive attention to model global contexts. This means that Fastformer considers the entire input

FastGCN

FastGCN: A Faster Way to Learn Graph Embeddings FastGCN is a recent improvement to the GCN model proposed by Kipf & Welling in 2016 for learning graph embeddings. Graph embeddings are a way to represent graphs as vectors or points in a high-dimensional space while preserving their structural properties. FastGCN improves upon the original algorithm by making it faster and addressing the memory bottleneck issue of GCN. GCN, or graph convolutional network, is a type of neural network that can be

FastMoE

FastMoE is a powerful distributed training system built on PyTorch that accelerates the training process of massive models with commonly used accelerators. This system is designed to provide a hierarchical interface to ensure the flexibility of model designs and the adaptability of different applications, such as Transformer-XL and Megatron-LM. What is FastMoE? FastMoE stands for Fast Mixture of Experts, a training system that distributes training for models across multiple nodes. Its primary

FastPitch

Are you tired of robotic-sounding text-to-speech models? Look no further than FastPitch - a state-of-the-art, fully-parallel model based on FastSpeech that produces natural-sounding speech by conditioning on fundamental frequency contours. What is FastPitch? FastPitch is a text-to-speech model that utilizes FastSpeech architecture and two feed-forward Transformer (FFTr) stacks to produce high-quality, natural-sounding speech. Unlike other text-to-speech models, FastPitch is fully-parallel, wh

FastSGT

Introduction to FastSGT FastSGT, or Fast Schema Guided Tracker, is a model designed for state tracking in goal-oriented dialogue systems. It uses a BERT-based approach, employing carry-over mechanisms for transferring values between slots and multi-head attention projections. Its NLU component consists of four main modules, including Utterance Encoder, Schema Encoder, State Decoder, and State Tracker. BERT was used for the Utterance Encoder and Schema Encoder. FastSGT: An Overview FastSGT, a

FastSpeech 2

FastSpeech 2: Improving Text-to-Speech Technology Text-to-speech (TTS) technology has greatly improved in recent years, but there is still a major challenge it faces called the one-to-many mapping problem. This refers to the issue where multiple speech variations correspond to the same input text, resulting in an inaccurate or robotic-sounding output. To address this problem, researchers have developed a new TTS model called FastSpeech 2, which aims to improve upon the original FastSpeech by di

FastSpeech 2s

FastSpeech 2s is an innovative text-to-speech model that generates speech directly from text during inference. This means that it skips mel-spectrogram generation and goes directly to waveform generation, making it a more efficient system. FastSpeech 2s has made two main design changes to the waveform decoder that have improved the model's capability. Main Design Changes The first major change that FastSpeech 2s has made is the use of adversarial training. Due to the difficulty of predicting

fastText

FastText: An Overview of Subword-based Word Embeddings FastText is a type of word embedding that utilizes subword information. Word embeddings are numerical representations of words that allow machines to understand natural language. They help improve the performance of various natural language processing (NLP) tasks, such as sentiment analysis, text classification, and machine translation. What are Word Embeddings? Word embeddings are numerical representations of words that capture their me

Fawkes

What is Fawkes? Fawkes is an image cloaking system designed to help people protect their images from unauthorized facial recognition models. This system helps users add imperceptible pixel-level changes to their own photos that will prevent their images from being identified by facial recognition models. How Fawkes Works Fawkes works by adding subtle changes to the user's images, making them undetectable to unauthorized facial recognition models. The system does this by inserting a small amo

FBNet Block

What is FBNet Block? FBNet Block is a type of image model block used in the FBNet architectures. It was discovered through DNAS neural architecture search. FBNet Block is made up of depthwise convolutions and a residual connection, which help to make the model more efficient and effective. How does FBNet Block work? FBNet Block works by using depthwise convolutions and residual connections. Depthwise convolutions are a type of convolutional layer that applies a single filter to each input ch

FBNet

Introduction to FBNet FBNet is a type of convolutional neural architecture that is designed using a neural architecture search called DNAS. It uses a basic image model block inspired by MobileNetv2 and consists of depthwise convolutions and an inverted residual structure. What is Convolutional Neural Architecture? Convolutional Neural Architecture refers to a type of artificial neural network that has been specifically designed to analyze image data. The convolutional neural architecture con

FCOS

Introduction to FCOS: An Anchor-Box Free Object Detection Model If you're someone who is interested in computer vision, you might have come across the term "object detection". Object detection is a crucial task in computer vision, where the objective is to detect objects present in an image or video. Over the past few years, many object detection models have been developed, and one such model is called FCOS. FCOS stands for Fully Convolutional One-Stage Object Detection, and it is an anchor-bo

FCPose

FCPose is a cutting-edge technology used for multi-person pose estimation. It is built on top of the FCOS object detector and eliminates the need for region of interest operations and post-processing grouping. Understanding FCPose FCPose is a fully convolutional framework that is used for multi-person pose estimation. It uses dynamic instance-aware convolutions to eliminate the need for ROI operations and grouping pre-processing. The dynamic keypoint heads used in FCPose are conditioned on ea

Feature-Aligned Person Search Network

Are you familiar with the concept of person search networks? If not, let us introduce you to AlignPS, or Feature-Aligned Person Search Network. What is AlignPS? AlignPS is an efficient anchor-free framework for person search. It uses a specific architecture, which is similar to the anchor-free detection model called FCOS. The model of AlignPS is designed to make it more focused on the re-identification (re-id) subtask. It does this by using an aligned feature aggregation (AFA) module. This m

Feature Fusion Module v1

Overview of FFMv1: A Feature Fusion Module from the M2Det Object Detection Model FFMv1, or Feature Fusion Module v1, is a component of the M2Det object detection model. Feature fusion modules play an essential role in creating the multi-level feature pyramid required for object detection. They utilize 1x1 convolution layers to reduce the channels of input features and a concatenation operation to combine feature maps. FFMv1 involves two feature maps from different scales in the backbone and a s

Feature Fusion Module v2

Feature Fusion Module v2, or FFMv2, is an important module in the object detection model known as M2Det. Its primary function is to combine the features from different levels to create a final, multi-level feature pyramid. What is M2Det? M2Det is an object detection model that aims to accurately and efficiently detect objects within an image. The model is based on the concept of feature pyramids, which involves combining features at multiple scales to achieve better accuracy. What is a feat

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Dependency Parsers Deraining Models Detection Assignment Rules Dialog Adaptation Dialog System Evaluation Dialogue State Trackers Dimensionality Reduction Discriminators Distillation Distributed Communication Distributed Methods Distributed Reinforcement Learning Distribution Approximation Distributions Document Embeddings Document Summary Evaluation Document Understanding Models Domain Adaptation Downsampling E-signing Efficient Planning Eligibility Traces Ensembling Entity Recognition Models Entity Retrieval Models Environment Design Methods Exaggeration Detection Models Expense Trackers Explainable CNNs Exploration Strategies Face Privacy Face Recognition Models Face Restoration Models Face-to-Face Translation Factorization Machines Feature Extractors Feature Matching Feature Pyramid Blocks Feature Upsampling Feedforward Networks Few-Shot Image-to-Image Translation Fine-Tuning Font Generation Models Fourier-related Transforms Free AI Tools Free Subscription Trackers Gated Linear 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Detection Models Mean Shift Clustering Medical Medical Image Models Medical waveform analysis Mesh-Based Simulation Models Meshing Meta-Learning Algorithms Methodology Miscellaneous Miscellaneous Components Mixture-of-Experts Model Compression Model Parallel Methods Momentum Rules Monocular Depth Estimation Models Motion Control Motion Prediction Models Multi-Modal Methods Multi-Object Tracking Models Multi-Scale Training Music Music source separation Music Transcription Natural Language Processing Natural Language Processing Courses Negative Sampling Network Shrinking Neural Architecture Search Neural Networks Neural Networks Courses Neural Search No Code AI No Code AI App Builders No Code Courses No Code Tools Non-Parametric Classification Non-Parametric Regression Normalization Numpy Courses Object Detection Models Object Detection Modules OCR Models Off-Policy TD Control Offline Reinforcement Learning Methods On-Policy TD Control One-Stage Object Detection Models Open-Domain 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