https://github.com/pytorch/pippy
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
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/pytorch/tau
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/pytorch/vision
https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/qiaopTDUN/mae-repo
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/quanmario0311/ViT_PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/qubvel/efficientnet
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/ra1ph2/Vision-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/rajneeshaggarwal/google-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ravi02512/efficientdet-keras
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rayanramoul/Visual-Transformer-PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/reyvaz/pneumothorax_detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/reyvaz/steel-defect-segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/richardaecn/cvpr18-inaturalist-transfer
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
https://github.com/rishikksh20/ResMLP-pytorch
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/rishikksh20/compact-convolution-transformer
https://github.com/rmwkwok/product_visual_search
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/roboflow/rf-detr
https://github.com/rohitgr7/tvmodels
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rollovd/LookSAM
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/ruiqirichard/eegeyenet-vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/rwightman/efficientnet-jax
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rwightman/gen-efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tnt.py
https://github.com/rwth-i6/fgvc/tree/master/elope_torch
ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding
https://github.com/s-chh/patchrot
Training data-efficient image transformers & distillation through attention
https://github.com/s-chh/pytorch-scratch-vision-transformer-vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/salluru007/papers
MetaFormer: A Unified Meta Framework for Fine-Grained Recognition
https://github.com/samsungsailmontreal/ghn3
https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/sangHa0411/VIT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/sayakpaul/A-Barebones-Image-Retrieval-System
https://github.com/sayakpaul/FunMatch-Distillation
https://github.com/sayakpaul/Sharpness-Aware-Minimization-TensorFlow
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/sayannath/ViT-Image-Classification
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/seanren96/Object-Detection
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.