Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/guoyongcs/NATv2
https://github.com/gupta-abhay/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/gupta-abhay/pytorch-frn
https://github.com/gurkirt/realtime-action-detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gusdnd852/ResNeXt-Parallel-Keras
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/gustavla/fractalnet
https://github.com/gyes00205/waymo_tf_object_detection
https://github.com/haaz77/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hackerchenzhuo/LaKo
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/haebeom-lee/amtfl
https://github.com/hafezgh/Music_genre_classification
Music Artist Classification with Convolutional Recurrent Neural Networks
https://github.com/hahahappyboy/GANForCartoon
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/haiquanlu/alphapruning
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
https://github.com/haisongzhang/roberta-tiny-cased
https://github.com/haithink/myWindowsDarknet
https://github.com/haiv-lab/ospcoop_imagenet-bg
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
https://github.com/haiyang-w/git
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/hamed-ahangari/Separation-Index-of-convolutional-layers-in-EfficientNet-B0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hamiddimyati/dd2424-deep-learning
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
https://github.com/hamzaMahdi/darknet
https://github.com/han-jia/lastshot
Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
https://github.com/hananshafi/vits-for-small-scale-datasets
https://github.com/hanfried/hanfried-bookmarks
https://github.com/hangg7/deformable-kernels
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
https://github.com/hanjianghu/pixel-wise-smoothing
Robustness Certification of Visual Perception Models via Camera Motion Smoothing
https://github.com/hankpark0706/darknet
https://github.com/hankyul2/EfficientNetV2-pytorch
https://github.com/hankyul2/maxvit-pytorch
https://github.com/hannaiiyanggit/unicon
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/hanoonaR/object-centric-ovd
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
https://github.com/hanxunh/sceloss-reproduce
Symmetric Cross Entropy for Robust Learning with Noisy Labels
https://github.com/hanzhanggit/StackGAN-inception-model
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/hanzhanggit/StackGAN-v2
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/hanzhaoml/mdan
https://github.com/haonanguo/remote-sensing-chatgpt
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.