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/zanedurante/vlm_benchmark
공식 코드Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)
https://github.com/zeiss-microscopy/BSConv
공식 코드https://github.com/zhangyuanhan-ai/bamboo
공식 코드Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
https://bitbucket.org/tsungyu/bcnn
https://github.com/04RR/SOTA-Vision
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/1170500804/tbmsl
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/2023-MindSpore-1/ms-code-151
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/autoaugment
https://github.com/2023-MindSpore-1/ms-code-189/tree/main/modelarts
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-195
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/ntsnet
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/TNT
https://github.com/2023-MindSpore-4/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-4/Code8/tree/main/autoaugment
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bit
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/dinov2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2024-MindSpore-1/Code6/tree/main/autoaugment
https://github.com/2024-MindSpore-1/Code7/tree/main/cct
https://github.com/2han9x1a0release/RLCC
https://github.com/4uiiurz1/pytorch-auto-augment
https://github.com/6210612757/facerecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/AI-NERC-NUPT/DDB
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/AI-NERC-NUPT/PFH-OSNet
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/AI-NERC-NUPT/PLR-OSNet
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/Abdulrahman-Adel/Real-Life-Violence-Detection
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Aedelon/ViT-PyTorch-Replication
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Alibaba-MIIL/TResNet
https://github.com/AlifAshrafee/ViT-pytorch-for-Cooking-State-Recognition
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/AndresPMD/Fine_Grained_Clf
https://github.com/AndresPMD/GCN_classification
Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval
https://github.com/ArdhenduBehera/cap
Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification
https://github.com/Ashay-20/TF-SAM-Sharpness-Aware-Minimization-Implementation
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/DeiT
Training data-efficient image transformers & distillation through attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.