https://github.com/keras-team/keras-io/blob/master/examples/vision/nnclr.py
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
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/kingcong/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/konstantinos-p/image_classification_SOTA
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/kornia/kornia
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/kspruthviraj/Plankiformer
Ensembles of Vision Transformers as a New Paradigm for Automated Classification in Ecology
https://github.com/kun-woo-park/Deeplearning_project_STL_10
https://github.com/labmlai/annotated_deep_learning_paper_implementations
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lalithjets/surgical_vqa
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/leaderj1001/Bag-of-MLP
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/leemsaebom/attention-guided-cam-visual-explanations-of-vision-transformer-guided-by-self-attention
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/leesb7426/cvpr2022-task-discrepancy-maximization-for-fine-grained-few-shot-classification
Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mlp_family
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
https://github.com/libffcv/ffcv-imagenet
https://github.com/lightly-ai/lightly
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/liuruiyang98/Jittor-MLP
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/liuxingwt/CLS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lpirola13/flower-recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lpirola13/flower_recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lucidrains/res-mlp-pytorch
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/lucidrains/transformer-in-transformer
https://github.com/lucidrains/vit-pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ludics/ViT-Retri
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lukas-blecher/LaTeX-OCR
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lukemelas/EfficientNet-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lukemelas/PyTorch-Pretrained-ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/luuchung/cifar-100
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lvweiwolf/efficientdet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lyqcom/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lyxxn0414/test-data-generation
https://github.com/lyy973/OSFA
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/macaodha/geo_prior
Presence-Only Geographical Priors for Fine-Grained Image Classification
https://github.com/machinelearning-goettingen/EfficientNet-ModelScaling
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mahmoodlab/hipt
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.