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/MindSpore-paper-code-3/code8/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-scientific/code-7/tree/main/VisionTransformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/NVIDIA/DeepLearningExamples
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/NZ99/transformer_in_transformer_flax
https://github.com/NiMlr/pynlqn
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/OML-Team/open-metric-learning
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Oguzhanercan/Vision-Transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/OrkhanHI/Grab-AI-Computer-Vision-Challenge
https://github.com/PRIS-CV/PCA-Net
Progressive Co-Attention Network for Fine-grained Visual Classification
https://github.com/PRIS-CV/Top-Down-Spatial-Attention-Loss
https://github.com/PRIS-CV/knowledge-transfer-based-FGVC
https://github.com/PaddlePaddle/FleetX/tree/develop/examples/pipeline
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/PaddlePaddle/PASSL
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/PaddlePaddle/PLSC/tree/master/task/classification/deit
Training data-efficient image transformers & distillation through attention
https://github.com/PaddlePaddle/PLSC/tree/master/task/classification/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/PaddlePaddle/PaddleClas
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/PaddlePaddle/PaddleDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/PeiqinZhuang/API-Net
Learning Attentive Pairwise Interaction for Fine-Grained Classification
https://github.com/PotatoSpudowski/CactiNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/QQBrowserVideoSearch/CBVS-UniCLIP
https://github.com/QuocThangNguyen/deep-metric-learning-tsinghua-dogs
https://github.com/RAF96/ifmo-2019-deep-learning-coursework
https://github.com/Rishit-dagli/Gradient-Centralization-TensorFlow
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
https://github.com/Rishit-dagli/Transformer-in-Transformer
https://github.com/Ryul0rd/compact-convolutional-transformer
https://github.com/SamsungSAILMontreal/ForestDiffusion
https://github.com/SforAiDl/vformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Shreyas-Bhat/CompactTransformers
https://github.com/SifatMd/Research-Papers
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SoojungYang/supervised_pretraining_GN_WS
https://github.com/SrinjaySarkar/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/SunDoge/efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SupreethRao99/VisionTransformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Thanusan19/Vision_Transformer
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.