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/avoroshilov/tf-squeezenet
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/awadalaa/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/awasthiabhijeet/Learning-From-Rules
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
https://github.com/awesomelemon/encas
https://github.com/aws-samples/amazon-sagemaker-visual-transformer
Visual Transformers: Token-based Image Representation and Processing for Computer Vision
https://github.com/awsaf49/gcvit-tf
https://github.com/awslabs/dynamic-training-with-apache-mxnet-on-aws
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources
https://github.com/awslabs/mlm-scoring
https://github.com/awslabs/sockeye
https://github.com/awur978/Autoencoder
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation
https://github.com/axelmukwena/biometricECG
https://github.com/axit54/Object-Recognition-using-the-All-CNN-network
https://github.com/axmoyal/Steganalysis-with-CNN
https://github.com/axrwl/bidirectional-cross-attention
Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers
https://github.com/ayanglab/swinganmr
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/ayanglab/swinmr
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/ayeshakhtar209/fashion-mnist
https://github.com/ayoolaolafenwa/PixelLib
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/ayoolaolafenwa/orangelib
https://github.com/ayulockin/Explore-NFNet
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/ayulockin/SwAV-TF
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
https://github.com/ayulockin/interpretabilitycnn
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ayushdabra/drone-images-semantic-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/dubai-satellite-imagery-segmentation
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/ayushdabra/lunar-landscape-images-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/retinal-oct-images-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/stanford-dogs-dataset-classification
https://github.com/ayushdewan/Latin-Alphabet-Recognition-Using-Convolutional-Neural-Networks-in-Tensorflow
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
https://github.com/ayushgupta9198/gan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushpalak/darknet_cokebottles
https://github.com/ayushpalak/darknet_multi_cokebottles
https://github.com/ayushpatidar/PR1-DenseNet
https://github.com/azamatkhid/mnasnet-pytorch
MnasNet: Platform-Aware Neural Architecture Search for Mobile
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.