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/calico/scnym
https://github.com/callmefish/NFnet
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/calmisential/TensorFlow2.0_Image_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/calmisential/TensorFlow2.0_InceptionV3
https://github.com/calmisential/TensorFlow2.0_ResNet
https://github.com/camenduru/llava-colab
https://github.com/cameronccohen/deep-ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/cameronchoi/r3det-copy
https://github.com/cammarasana123/weightedconvolution
Optimal Weighted Convolution for Classification and Denosing
https://github.com/camp-explain-ai/style-chexplain
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
https://github.com/candy4869/2014
https://github.com/canerozer/qct
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/canturan10/satellighte
https://github.com/caojoshua/Plastic-Recyclability
https://github.com/caoquanjie/ConditionalLearnToPayAttention
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
https://github.com/caoquanjie/LearnToPayAttention
https://github.com/caotians1/OD-test-master
A Less Biased Evaluation of Out-of-distribution Sample Detectors
https://github.com/captain-pool/DC-GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/captaindario/dakanji-single-kanji-recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/carlfu127/Keras-NTS-Net
https://github.com/carlosb1/upc-aidl-19-team4
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/carolgithubv1/convnets-keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/carpedm20/DCGAN-tensorflow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/carpedm20/ENAS-pytorch
https://github.com/castorini/Castor
https://github.com/castorini/deebert
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/castorini/earlyexiting-monobert
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/castorini/hedwig
https://github.com/cavedunissin/deeplearning_robot_jetson_nano
https://github.com/cbg-ethz/bmi
https://github.com/ccc013/DeepLearning_Notes
https://github.com/cchinchristopherj/Right-Whale-Unsupervised-Model
https://github.com/cdluminate/advrank
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/cdluminate/advrank-pub
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/cdoersch/deepcontext
Data-dependent Initializations of Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.