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/htdt/self-supervised
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/htso/Pedestrian_in_few_shots
https://github.com/hu-my/taskattributedistance
https://github.com/huang-research-group/globalfsl2019
https://github.com/huangleiBuaa/DecorrelatedBN
https://github.com/huanranchen/CommonWeaknessDetection
Rethinking Model Ensemble in Transfer-based Adversarial Attacks
https://github.com/huawei-noah/KD-NLP/tree/main/Annealing_KD
https://github.com/huckiyang/MOT-Kuzushiji-Fashion-Video
https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/maxxvit.py
https://github.com/huggingface/pytorch-openai-transformer-lm
https://github.com/huggingface/transformers
https://github.com/huggingface/transformers/tree/main/src/transformers/models/data2vec
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/hughperkins/DeepCL
https://github.com/hujinxinb/face_detect
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/humanlab/rare-class-AL
Active Learning for Convolutional Neural Networks: A Core-Set Approach
https://github.com/hun1204/DeepLearning_Visualization_Basic
https://github.com/hunnurjirao/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/hunto/image_classification_sota
Diverse Branch Block: Building a Convolution as an Inception-like Unit
https://github.com/hursung1/DeepGenerativeReplay
https://github.com/hustic/TinyImageSet
https://github.com/hustvl/sparseinst
https://github.com/huybik/CNN-from-first-principle
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/huyng/incertae
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/huynhtuan17ti/AI4VN-Hackathon2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/huytranvan2010/RetinaNet-Understanding
https://github.com/huzpsb/EffTransfer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hw666666666666/BigTransfer
https://github.com/hw666666666666/CoAtNet
CoAtNet: Marrying Convolution and Attention for All Data Sizes
https://github.com/hwalsuklee/tensorflow-mnist-AAE
https://github.com/hwalsuklee/tensorflow-mnist-MLP-batch_normalization-weight_initializers
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
https://github.com/hweersot/darknet-custom
https://github.com/hwk0702/keras2torch/tree/main/Computer_Vision/Model_Soup
https://github.com/hwzwx1190522/efficientnetv2/tree/main/efficientnetv2
https://github.com/hwzwx1190522/inceptionv3
https://github.com/hwzwx1190522/inceptionv4
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.