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/DrMMZ/RetinaNet
https://github.com/DrRyanHuang/VGGNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DreamInvoker/LookaheadOptimizer-mx-implementation
https://github.com/Dref360/spectral_metric
https://github.com/DuaneNielsen/keypoints
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/DucQuan12/Classification-with-activation-mish
https://github.com/Duplums/bhb10k-dl-benchmark
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Dycollapsar/Attention-Based-for-Medicalimaging
https://github.com/Dylan-get/Feature-Learning-RotNet
Unsupervised Representation Learning by Predicting Image Rotations
https://github.com/DylanMuir/fmin_adam
Improving neural networks by preventing co-adaptation of feature detectors
https://github.com/E-DEEP/PapersReview
https://github.com/EMalagoli92/CvT-TensorFlow
https://github.com/EMalagoli92/GCViT-TensorFlow
https://github.com/EMalagoli92/VAN-Classification-TensorFlow
https://github.com/EMsnap/RobotSorting
https://github.com/ENSTA-U2IS/MUAD-Dataset
https://github.com/EPFL-VILAB/XDEnsembles
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/EXJUSTICE/Cross-Domain_Classification_Using_StyleTransfer
Cross-Domain Image Classification through Neural-Style Transfer Data Augmentation
https://github.com/EchoItLiu/SelfGait
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EdinburghNLP/XSum
https://github.com/Edmonton-School-of-AI/ml5-Simple-Image-Classification
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/EdwardLin2014/CNN-with-IBM-for-Singing-Voice-Separation
https://github.com/EdwinAlegria/object_semantic_deeplabv3
https://github.com/Egesabanci/capsuleNetworks
https://github.com/EiroaMD/final_project_IH_pneumonia
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ElementAI/TADAM
TADAM: Task dependent adaptive metric for improved few-shot learning
https://github.com/ElementAI/am3
https://github.com/ElenaRyumina/EMO-AffectNetModel
https://github.com/Eljefemasao/Customed-Object-Detector
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Eljefemasao/MarkNet-ObjectDetector
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Elman295/MLP-Mixer-for-MNIST-Classification
https://github.com/Elman295/Maximum-Classifier-Discrepancy-for-Unsupervised-Domain-Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/Elman295/Unsupervised-Domain-Adaptation-by-Backpropagation-
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.