https://github.com/datawiz1984/malaria-model
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/datoboat/Vehicle-Detection
https://github.com/davda54/chainer-wideresnet
https://github.com/davda54/sam
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/daveboat/pytorch_focal_loss
https://github.com/daveboat/spp
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/daveboat/structured_label_inference
https://github.com/david3684/adarank
https://github.com/davidczy/supcon_gamma
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/davidhalladay/CE-compressive-sensing-on-generative-model-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/davidstap/AttnGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/davidsvy/hard-negative-mixing
Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/davidtellez/contrastive-predictive-coding
https://github.com/davisking/dlib-models
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/dayfixer/darknet-comment
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/db-Lee/Meta-GMVAE
https://github.com/dbaranchuk/memory-efficient-maml
https://github.com/dbash/zerowaste
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/dbensoussan/rsna
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/dblN/stochastic_depth_keras
https://github.com/dbolya/tomesd
https://github.com/dc17540/darknet
https://github.com/dc3ea9f/RegionViT
RegionViT: Regional-to-Local Attention for Vision Transformers
https://github.com/ddddwee1/Octave-Convolution
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/ddman1101/EDM-subgenre-classifier
Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features
https://github.com/dead-mazai/avia-missing-airplanes
https://github.com/debajyotid2/noisy_prediction_calibration
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
https://github.com/debanjanxy/GNR-652
https://github.com/decodepfl/deepdiscoph
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
https://github.com/dedeswim/vits-robustness-torch
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
https://github.com/dedhiaparth98/capsule-network
https://github.com/deep-learning-algorithm/LightWeightCNN
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/deepak112/Keras-SRGAN
https://github.com/deepaks2112/vat_lds
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
https://github.com/deeplearning-wisc/gradnorm_ood
The iNaturalist Species Classification and Detection Dataset
https://github.com/deeplearning-wisc/knn-ood
The iNaturalist Species Classification and Detection Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.