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/Burf/CoAtNet-Tensorflow2
CoAtNet: Marrying Convolution and Attention for All Data Sizes
https://github.com/Burf/EfficientNet-Lite-Tensorflow2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Burf/ModelSoups
https://github.com/Burf/ResNeSt-Tensorflow2
https://github.com/Burf/ResNet-Tensorflow2
https://github.com/Burf/SwinTransformer-Tensorflow2
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Burf/VisionTransformer-Tensorflow2
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Burf/tfdetection
https://github.com/BurguerJohn/global_perceptual_similarity_loss
https://github.com/BurguerJohn/torch-felix
https://github.com/ByakuyaUncia/DenseNetUncia
https://github.com/ByungKwanLee/Causal-Unsupervised-Segmentation
https://github.com/CESARDELATORRE/ComputerVisionTensorFlowMLNET
https://github.com/CHDyshli/HrSegNet4CrackSegmentation
Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping
https://github.com/CHeggan/Few-Shot-Classification-for-Audio-Evaluation
https://github.com/CKPOON0619/Kaggle2Sigma
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/COAOX/Cifar-Prototypical
https://github.com/COGMAR/RotEqNet
https://github.com/COMP6248-Reproducability-Challenge/DEFENDING-AGAINST-PHYSICALLY-REALIZABLE-ATTACKS-ON-IMAGE-CLASSIFICATION
Defending Against Physically Realizable Attacks on Image Classification
https://github.com/COMP6248-Reproducability-Challenge/LookaheadOptimizer
https://github.com/COMP6248-Reproducability-Challenge/Network-Deconvolution-in-CNNs
https://github.com/CPJKU/cca_layer
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/CPUFronz/CapsVoxGAN
https://github.com/CPones/Paddle_CaiT
https://github.com/CRIGIM/darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/CSAILVision/semantic-segmentation-pytorch
https://github.com/CV51GO/FishNet_Megengine
https://github.com/CV51GO/GhostNet_Megengine
https://github.com/CVxTz/face_age_gender
https://github.com/CWanli/myencoding
https://github.com/Cadden/paddle-adver
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Cadene/pretrained-models.pytorch
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/CahideSara/deneme
https://github.com/Callifrey/Paddle-CIC
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.