https://github.com/triple7/Keras-WGAN-RGB-128x128
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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/tsing-cv/EfficientNet-tensorflow-eager
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ttt496/VisionTransformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ttt496/vit-pytorch
Training data-efficient image transformers & distillation through attention
https://github.com/tuvovan/Vision_Transformer_Keras
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/tw-yuhsi/a-new-perspective-for-shuttlecock-hitting-event-detection
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ultralytics/yolov5
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/uygarkurt/ViT-PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/uzi0espil/research-papers-implementation/tree/master/Vision%20Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/vladthesav/MoldAI
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/vturrisi/solo-learn
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
https://github.com/wangermeng2021/FastClassification
https://github.com/wangermeng2021/Scaled-YOLOv4-tensorflow2
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/wangermeng2021/SnapMix-tensorflow2
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
https://github.com/wangguanan/light-reid
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/wangyi111/international-archaeology-ai-challenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/wangyz1608/knowledge-distillation-via-nd
Training data-efficient image transformers & distillation through attention
https://github.com/whj363636/Adversarial-attack-on-Person-ReID-With-Deep-Mis-Ranking
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/willard-yuan/video-text-retrieval-papers
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/wish44165/A-New-Perspective-for-Shuttlecock-Hitting-Event-Detection
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/woctezuma/steam-CLIP
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/wusaifei/HWCC_image_classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/wvinzh/WS_DAN_PyTorch
https://github.com/wwhio/megmodels
https://github.com/xcnkx/fine_grained_classification
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
https://github.com/xiuyu0000/new_papers_codes/tree/main/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/xiuyu0000/papers_with_examples/tree/main/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/backbones/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/xmu-xiaoma666/External-Attention-pytorch
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/xslidi/EfficientNets_ddl_apex
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/yakhyo/EfficientNet-PyTorch
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/yangyucheng000/University/tree/main/model-3/regnet
https://github.com/yangyucheng000/University/tree/main/model-3/resnet
https://github.com/yeyinthtoon/tf2-resmlp
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/youngwoo-yoon/Practical-tips-for-lightweight-deep-learning
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ys-zong/medfair
Sharpness-Aware Minimization for Efficiently Improving Generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.