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/RajeevReddyIlavala/Low-Shot-Learning-Learning
Low-shot Visual Recognition by Shrinking and Hallucinating Features
https://github.com/Rajjeshwar/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/RajuGudhe/Cancer_Tissue_Detection
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/RameshBattu/Convolutional-Neural-Network-on-CIFAR-10-DataSet
https://github.com/RamposhPidrov/waifu2face
https://github.com/Ranchofromxgd/DCGAN_mnist
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Randl/MobileNetV3-pytorch
https://github.com/Ranja-S/sensitivity
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
https://github.com/RashadGarayev/ColorizeImage
https://github.com/RasmusAndersen/grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/RayDeeA/ibinn_imagenet
Generative Classifiers as a Basis for Trustworthy Image Classification
https://github.com/RayXie29/SENet_Keras
https://github.com/Rayryu/CNN-based-sentimental-analysis-multiclass-classification
https://github.com/Rayyan-Akhtar/XLSR
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices
https://github.com/RealHulubulu/openCVObjectDetectionRPi
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Reatris/Capsule_Net
https://github.com/Reatris/Visualizing-and-Understanding-Convolutional-Networks
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
Cost-Effective Active Learning for Deep Image Classification
https://github.com/RemiArbache/style-transfer-M2
https://github.com/Res2Net/Res2Net-PoolNet
https://github.com/Res2Net/Res2Net-Pose-Estimation
https://github.com/Res2Net/Res2Net-PretrainedModels
https://github.com/Res2Net/Res2Net-maskrcnn
https://github.com/Res2Net/mmdetection
https://github.com/ReshinthAdith/BYOL-Pytorch
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/ReshmikaD/Artistic-Style-Transfer-CNN
https://github.com/Reza-Safdari/SimSiam
https://github.com/RichardMathewsII/YOLBO
https://github.com/RikoLi/gan-acgface
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Rintarooo/PSPNet
https://github.com/Riroaki/CapsNet
https://github.com/Rishit-dagli/ConvMixer-torch2tf
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.