https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/ResMLP
ResMLP: Feedforward networks for image classification with data-efficient training
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/BR-IDL/PaddleViT/blob/main/image_classification/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/BaiqiangGit/15minCode
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/BebDong/MXNetSeg
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/BenjiKCF/EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/BobMcDear/pytorch-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/BrianPulfer/PapersReimplementations
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Burf/EfficientNet-Lite-Tensorflow2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Burf/VisionTransformer-Tensorflow2
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/BurguerJohn/global_perceptual_similarity_loss
https://github.com/BurguerJohn/torch-felix
https://github.com/ByungKwanLee/Causal-Unsupervised-Segmentation
https://github.com/CV51GO/FishNet_Megengine
https://github.com/ChintanThacker/Facial_Expression_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Cyprien0105/DataScience
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DableUTeeF/keras-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DavidLandup0/deepvision
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Deci-AI/super-gradients
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DeepBrainsMe/FSnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DeepVoltaire/AutoAugment
https://github.com/DiKorsch/l1_parts
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
https://github.com/Dichao-Liu/CMAL
https://github.com/Dichao-Liu/CRA-CNN
https://github.com/DominikBatic/EndoViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-SWAG
Revisiting Weakly Supervised Pre-Training of Visual Perception Models
https://github.com/FlyingMoon-GitHub/ACNet
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
https://github.com/Guang000/Awesome-Dataset-Distillation
https://github.com/HO4X/TSR_JetsonTX2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/HamadYA/GhostFaceNets
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
https://github.com/HyeonhoonLee/MAIC2021_Sleep
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/HzcIrving/DeepLearning_PlayGround/tree/main/VIT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/IMvision12/keras-vision-models
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Imageomics/Prompt_CAM
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis
https://github.com/IssamLaradji/sps
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
https://github.com/JDAI-CV/DCL
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
https://github.com/JHKim-snu/PGA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.