Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fk798/dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/fla-org/flash-linear-attention
https://github.com/flexibility2/Pytorch-Implementation-for-RelationNet
https://github.com/flexible-fl/flex-nlp
https://github.com/flj19951219/mamba_dose
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
https://github.com/float256/rectified-adam-keras
https://github.com/floodsung/LearningToCompare_FSL
https://github.com/flowritecom/flow-merge
https://github.com/flrngel/cpc-tensorflow
https://github.com/flukeskywalker/highway-networks
https://github.com/flyingsheepbin/pet-biometrics
https://github.com/flymin/darts
https://github.com/flytocc/ConvNeXt-paddle
https://github.com/flytocc/CycleMLP-paddle
https://github.com/flytocc/PaddleClas
https://github.com/flytocc/TokenLabeling-paddle
All Tokens Matter: Token Labeling for Training Better Vision Transformers
https://github.com/flytocc/mae-paddle
https://github.com/flyyufelix/DenseNet-Keras
https://github.com/fmcp/EndToEndIncrementalLearning
https://github.com/fmi-basel/implicit-var-reg
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/fmorenovr/Semi-Supervised-Learning_with_GAN_Keras
https://github.com/fmu2/PyTorch-MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/fogfog2/packnet
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/foolpanda/deepFC
https://github.com/foolyc/Meta-SGD
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/forcefulowl/image_classification
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/forcesh/SupContrast
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/forgi86/lru-reduction
Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/formal-verification-research/NJSMA
https://github.com/formal-verification-research/verapak
https://github.com/fpsluozi/smd-smos
https://github.com/francis-kang/handson-unsupervised-learning
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/francislata/Sign-Language-Digits-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/frank-roesler/Image_Segmentation
https://github.com/frankkramer-lab/aucmedi
Standardized Medical Image Classification across Medical Disciplines
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.