Papers with Code 아카이브
Fine-Grained Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 35
- 보존된 평가 기록
- 374
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/semskurto/APTOS
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/septmars/DL
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/serfaniane/siamese_person_re_id
https://github.com/setharram/facenet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seujung/pytorch-vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/seunghwan1228/CNN_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/shahrukhx01/ocr-test
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/shijianjian/efficientnet-pytorch-3d
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/shinya7y/UniverseNet
https://github.com/simon20010923/DDAMFN
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/sithu31296/semantic-segmentation
https://github.com/skchen1993/TrangFG
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/skhetarpal/Prostate_Cancer_Grade_Assessment
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/sliao-mi-luku/Galaxy-Zoo-Classification
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/smitheric95/MoCoViT-PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/smu-ivpl/DeepfakeDetection
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/sneakatyou/ViT-Tensorflow-2.0
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/soumik-kanad/diffssl
https://github.com/soumik12345/Vision-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/staghado/vit.cpp
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/stevenwalton/scs-cct
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/tahmid0007/VisionTransformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/tajamul21/fate
https://github.com/tarikdgny/Optical_Coherence_Tomography
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tau-yihouxiang/WS_DAN
https://github.com/tensorflow/lingvo
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/tensorflow/models/tree/master/research/autoaugment
https://github.com/tensorflow/tpu/tree/master/models/official/detection
https://github.com/tianhai123/vit-pytorch
Training data-efficient image transformers & distillation through attention
https://github.com/timH6502/VisionTransformer-PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/tintn/vision-transformer-from-scratch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/titu1994/keras-efficientnets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/toanbkmt/EfficientnetFruitDetect
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tommarvoloriddle/Bilinear-CNN-Tensorflow2.4-implementation
https://github.com/towhee-io/towhee
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.