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/altaga/Pytorch-Driving-Guardian
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/alvarobartt/serving-pytorch-models
https://github.com/alvinwan/shiftresnet-cifar
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
https://github.com/alyaxey/bert_for_intent_recognition
Multi-Sample Dropout for Accelerated Training and Better Generalization
https://github.com/alzmzyy/pointtransformer
https://github.com/amamov/vgg16-vs-resnet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aman-garg0001/fruit-classifier
https://github.com/amaranth819/dcgan-cifar10-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/amazingcodeLYL/Traffic_signs_detection_darket
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/amazon-research/bigdetection
A Simple Semi-Supervised Learning Framework for Object Detection
https://github.com/amazon-research/dse
https://github.com/amazon-research/long-short-term-transformer
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/amazon-research/stochastic-backpropagation
https://github.com/amazon-science/bigdetection
A Simple Semi-Supervised Learning Framework for Object Detection
https://github.com/amazon-science/long-short-term-transformer
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/amazon-science/semi-vit
https://github.com/ambityga/memsem
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ambujojha/SemiSupervisedLearning
https://github.com/amcs1729/Predicting-cloud-CPU-usage-on-Azure-data
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/amdegroot/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amerch/CIFAR100-Training
https://github.com/aminekha/AI-For-2022
https://github.com/amir7d0/InfoGAN
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/amiratag/multiaccuracyboost
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
https://github.com/amirbar/FilterResponseNormalization
https://github.com/amirdy/dog-breed-classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/amiryanj/socialways
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/amitakamath/hard_positives
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/amitakamath/whatsup_vlms
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/amitgupta223/Real-Time-Face-Mask-Detection-using-OpenCV-on-MacOS
https://github.com/amittiwary42/target-detection-eeg-signals
https://github.com/amobiny/Recurrent_Attention_Model
https://github.com/amogh7joshi/engagement-detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/fer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/media-vision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.