Papers with Code 아카이브
Fine-Grained Image Classification
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Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.
<span style="color:grey; opacity: 0.6">( Image credit: Looking for the Devil in the Details )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/metaclip
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/facebookresearch/pycls
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/faustomorales/vit-keras
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/federicopozzi33/MobileOne-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/filaPro/visda2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/filipmu/Kaggle-APTOS-2019-Blindness
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/freeSubmission/SDB
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/frgfm/Holocron
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
https://github.com/gatech-eic/vitcod
Training data-efficient image transformers & distillation through attention
https://github.com/gengchenmai/space2vec
Presence-Only Geographical Priors for Fine-Grained Image Classification
https://github.com/gilesagnel/FGVC
https://github.com/gimme1dollar/vision-transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/github-luffy/PFLD_68points_Pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gmum/dl-mo-2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/gnoses/ViT_examples
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/gojkoc54/HOG_SVM_Vehicle_Detection
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
https://github.com/gomezzz/MSMatch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gomezzz/distmsmatch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gorkaydemir/DINOSAUR
https://github.com/gouthamvgk/coreml_conversion_hub
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gregorbachmann/scaling_mlps
https://github.com/gupta-abhay/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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/haysacks/spot-the-coin
https://github.com/hbchen121/dgreid
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/holdfire/CLS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/holdfire/FAS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/houstonsantos/CassavaLeafDisease
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hu-my/taskattributedistance
https://github.com/huggingface/transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/hun1204/DeepLearning_Visualization_Basic
https://github.com/hunto/dist_kd
https://github.com/hustvl/vim
Training data-efficient image transformers & distillation through attention
https://github.com/huynhtuan17ti/AI4VN-Hackathon2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.