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/akira-l/mhem
https://github.com/alcunha/geo_prior_tf
Presence-Only Geographical Priors for Fine-Grained Image Classification
https://github.com/alcunha/iwildcam2021ufam
Presence-Only Geographical Priors for Fine-Grained Image Classification
https://github.com/alessiomora/unlearning_fl
Training data-efficient image transformers & distillation through attention
https://github.com/alibaba/EasyCV
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/alililia/ms_extend/tree/main/gpu_efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/alililia/vit_base_Ascend
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/alililia/vit_base_GPU
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/alondj/Pytorch-Gpipe
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/amirdy/dog-breed-classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/arindam-1991/i2-hofi
https://github.com/arkel23/PyTorch-Pretrained-ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/armin-azh/3DefficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/asad-62/IVP-DNN
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/asarigun/TransGAN
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ashishpatel26/Vision-Transformer-Keras-Tensorflow-Pytorch-Examples
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/asrafulashiq/deit-custom
Training data-efficient image transformers & distillation through attention
https://github.com/asyml/vision-transformer-pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/avinash31d/paper-implementations/tree/main/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/awesomelemon/encas
https://github.com/barisozmen/deepaugment
https://github.com/batsresearch/taglets
https://github.com/beneroth13/dinov2
https://github.com/beresandras/contrastive-classification-keras
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
https://github.com/bespontaneous/proteus-pytorch
https://github.com/bethgelab/InDomainGeneralizationBenchmark
https://github.com/biqiwhu/cgl
https://github.com/borealisai/perturbed-forgetting
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/brohrer/sharpened-cosine-similarity
https://github.com/bshantam97/Attention_Based_Networks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/buiquangmanhhp1999/age_gender_estimation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/buyeah1109/KEN
https://github.com/buyeah1109/finc
https://github.com/canturan10/satellighte
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/captaindario/dakanji-single-kanji-recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.