Image Classification
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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/herbps10/adaptiveconformal
Improved Online Conformal Prediction via Strongly Adaptive Online Learning
https://github.com/hershd23/ObjectLocalizer
https://github.com/hewanshrestha/why-self-supervision-in-time
https://github.com/hexhowells/Neural-Network-Implementations/tree/main/models/ParNet
https://github.com/hey-yahei/CondConv.MXNet
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
https://github.com/hfahrudin/reptile_implement_tf2
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/hfassold/mbtrim
https://github.com/hgupta01/svm_classification_keras
https://github.com/hh-xiaohu/Image-augementation-pytorch
https://github.com/hhaAndroid/u2net
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/hhl60492/deep-kernel-transfer
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
https://github.com/hhuaibo/darknet
https://github.com/hiarindam/document-image-classification-TL-SG
https://github.com/hiarsal/dae-gan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/hieu2906090/deep-learning-in-js
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/highwaywu/tianchi-fft2
https://github.com/hik-lab/ssod
A Simple Semi-Supervised Learning Framework for Object Detection
https://github.com/hikapok/tf-senet
https://github.com/hikapok/xception_tensorflow
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/hikvision-research/SSOD
A Simple Semi-Supervised Learning Framework for Object Detection
https://github.com/hillup/active_learning
Active Learning for Convolutional Neural Networks: A Core-Set Approach
https://github.com/himanshu-dutta/MLPMixer-pytorch
https://github.com/hiram64/temporal-ensembling-semi-supervised
https://github.com/hirohiro23/Darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/hisiter97/darknet
https://github.com/hitchingsh/ml-examples
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/hkbu-vscomputing/2022_mm_dmae-mocap
https://github.com/hkchengrex/TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/hku-medai/cdpmil
https://github.com/hku-medai/mgca
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
https://github.com/hkuds/easyrec
https://github.com/hl-hanlin/smkd
https://github.com/hli2020/nn_capsulation
https://github.com/hli2020/nn_encapsulation
https://github.com/hlidmhkust/VCCIB
Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.