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/infinitemugen/MAML-Pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/inhopark94/ihpark
https://github.com/inkplatform/InfoGAN-PyTorch
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/innat/External-Attention-TensorFlow
Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks
https://github.com/innat/HybridModel-GradCAM
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/innat/LearnedResizer-Vision-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/innat/VideoMAE
https://github.com/innat/VideoSwin
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/insop/pytorch-hackathon
https://github.com/intel-isl/MiDaS
MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation
https://github.com/intelligent-vehicles/bevdriver
https://github.com/interestingzhuo/foodretreival
https://github.com/intsco/am-segmentation
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/ioanvl/1d_squeeze_excitation
https://github.com/irmvlab/point-mamba
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
https://github.com/is90057/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/isaaccasm/GradCAM-keras
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/isaaccorley/hydro-foundation-model
https://github.com/isaaccorley/mlp-mixer-pytorch
https://github.com/isaaccorley/segmenter-pytorch
https://github.com/isaacgerg/keras_odds_and_ends
https://github.com/isaachaw/GrabCarRecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/isaacschaal/SG_training
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/isakdiaz/PlantNet_MobileNetV2
https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning
https://github.com/iskandari/darknet
https://github.com/isl-org/Open3D-ML
https://github.com/islab-sophia/odisal
Influence of Image Classification Accuracy on Saliency Map Estimation
https://github.com/islamosmanubc/MedMAE
https://github.com/islaterm/you-activated-my-neuron
https://github.com/ismailuddin/gradcam-tensorflow-2
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/isosnovik/sesn
https://github.com/ispamm/PRER
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
https://github.com/ispamm/htorch
https://github.com/istvanmegyeri/audio_tagging
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.