https://github.com/deeplearning-wisc/npos
The iNaturalist Species Classification and Detection Dataset
Papers with Code 아카이브
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/deeplearning-wisc/react
The iNaturalist Species Classification and Detection Dataset
https://github.com/deeplearning-wisc/snn
The iNaturalist Species Classification and Detection Dataset
https://github.com/deeplearning987/DoodleClassifier
https://github.com/deeplearningforfun/OctConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/deeplearnproject/darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/deepmind/deepmind-research/tree/master/transporter
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/deepmind/dm-haiku
https://github.com/deepmind/kinetics-i3d
https://github.com/deepmind/optax
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
https://github.com/deepmodeling/deepmd-kit
https://github.com/deepquestai/fire-smoke-dataset
https://github.com/deepsense-ai/unblackboxing_webinar
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/deepshwang/swn_gcn
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
https://github.com/deepshwang/wn_gcn
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
https://github.com/delmalih/MIAS-mammography-obj-detection
https://github.com/delta6189/Anime-Sketch-Colorizer
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
https://github.com/delyan-boychev/grad-cache-con-learning
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/demul/AlexNet
https://github.com/demul/ResidualNet
https://github.com/demul/auto_colorization_project/tree/master/colorful_image_colorization
https://github.com/dengpingfan/fsgan
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/denizyuret/playground
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/denru01/netadapt
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
https://github.com/deontaepharr/Residual-Attention-Network
https://github.com/derrickwang005/mosrep
https://github.com/detectionteamucas/retinanet_tensorflow
https://github.com/developer0hye/SKNet-PyTorch
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/developmentseed/looking-glass-pub
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/devhemza/BERTweet_sentiment_analysis
https://github.com/devrimcavusoglu/std
https://github.com/dewenzeng/clsp
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/dhaalves/CEAL_keras
Cost-Effective Active Learning for Deep Image Classification
https://github.com/dhansmair/flamingo-mini
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/dhruvpatel108/GANPriors
https://github.com/dhx000/DGM_project
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.