Papers with Code 아카이브

Image Recognition

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Tubaher/lpr

LPRNet: License Plate Recognition via Deep Neural Networks

https://github.com/ZF4444/MMAL-Net

Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

https://github.com/ZosoV/license-plate-recognition

LPRNet: License Plate Recognition via Deep Neural Networks

https://github.com/brightyoun/LPSR-Recognition

Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution

https://github.com/brightyoun/TITS-LPST

An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector

https://github.com/deeplearning-wisc/cider

Describing Textures in the Wild

https://github.com/deeplearning-wisc/dice

Describing Textures in the Wild

https://github.com/deeplearning-wisc/gradnorm_ood

Describing Textures in the Wild

https://github.com/deeplearning-wisc/knn-ood

Describing Textures in the Wild

https://github.com/deeplearning-wisc/large_scale_ood

Describing Textures in the Wild

https://github.com/deeplearning-wisc/npos

Describing Textures in the Wild

https://github.com/deeplearning-wisc/react

Describing Textures in the Wild

https://github.com/deeplearning-wisc/snn

Describing Textures in the Wild

https://github.com/deercoder/DeepFood

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

https://github.com/doc-doc/NExT-OE

PaLI-X: On Scaling up a Multilingual Vision and Language Model

https://github.com/dreamercv/HSResNet-MMAL

Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

https://github.com/etmwb/cvsegmentation

Deep TEN: Texture Encoding Network

https://github.com/huzexi/huzexi

Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels

https://github.com/ikigai-aa/Automatic-License-Plate-Recognition

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

https://github.com/jianshen92/stanford-car-grab-challenge

Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

https://github.com/jiaxue1993/pytorch-material-classification

Differential Viewpoints for Ground Terrain Material Recognition

https://github.com/jimzai/mode-ood

Describing Textures in the Wild

https://github.com/kebasaa/SCIO-read

Classification of Household Materials via Spectroscopy

https://github.com/kmaninis/pytorch-encoding

Deep TEN: Texture Encoding Network

https://github.com/kornia/kornia

Local Patch AutoAugment with Multi-Agent Collaboration

https://github.com/kyegomez/PALI

PaLI-X: On Scaling up a Multilingual Vision and Language Model

https://github.com/lyl8213/Plate_Recognition-LPRnet

LPRNet: License Plate Recognition via Deep Neural Networks

https://github.com/mapleleaf6/zode

Describing Textures in the Wild

https://github.com/mesakarghm/LPRNET

LPRNet: License Plate Recognition via Deep Neural Networks

https://github.com/msfuxian/DualAttentionNet

Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples

https://github.com/mv-lab/ViT-FGVC8

Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

https://github.com/open-vision-language/oven

Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities

https://github.com/osmr/imgclsmob

Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

https://github.com/puallee/HOI-Net

https://github.com/sagieppel/MatSim-Dataset-Generator-Scripts-And-Neural-net

One-shot recognition of any material anywhere using contrastive learning with physics-based rendering

https://github.com/sagieppel/testing-large-vision-language-models-lvlm-on-visual-questions

Shape and Texture Recognition in Large Vision-Language Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.