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/idstcv/ZenNAS
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
https://github.com/idt-iti/xai-deepfakes
An adversarial attack approach for eXplainable AI evaluation on deepfake detection models
https://github.com/iduta/pyconvsegnet
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/iejMac/ScriptWriter
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ifrit98/bengaliai
https://github.com/ifsheldon/MPL_Lightning
https://github.com/igitugraz/outputcodematching
https://github.com/ihaeyong/maximum-margin-ldam
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
https://github.com/ihamdi/Covid-xRay-Classification
https://github.com/ihsuy/Train-by-Reconnect
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
https://github.com/iinemo/lm-polygraph
Uncertainty Estimation in Autoregressive Structured Prediction
https://github.com/iirishikaii/cleverhans
https://github.com/ikhlestov/vision_networks
https://github.com/ikigai-aa/Face-Mask-Detector-using-MobileNetV2
https://github.com/ikuto056/Estimating_Distance
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
https://github.com/ildoonet/cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/ildoonet/pytorch-randaugment
https://github.com/ildoonet/unsupervised-data-augmentation
https://github.com/ilkhem/icebeem
https://github.com/ilmimris/bangkit-bgd2a-virus-covid-vs-noncovid
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
https://github.com/ilonatommy/DLR_FacialEmotionRecognition
Image Classification by Reinforcement Learning with Two-State Q-Learning
https://github.com/ilyak93/GAIN-pytorch
https://github.com/ilyassmoummad/ProtoCLR
https://github.com/im-ant/Capsules-Guys
https://github.com/imad08/MLP-Mixer
https://github.com/imad08/Repvgg_pytorch
https://github.com/imanpalsingh/COVID-19-Detection--from-X-rays
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
https://github.com/imanpalsingh/COVID-19-Diagnosis-using-Convolutional-and-Generative-models
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
https://github.com/imavijit/detect_it
https://github.com/imkoushik22/Walmart-Hackathon
https://github.com/impiga/plain-detr
https://github.com/impira/docquery
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
https://github.com/imrahulr/adversarial_robustness_pytorch
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/imshamsher/darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/inars/developing_mc_for_zsl
Evaluation of Output Embeddings for Fine-Grained Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.