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/code-implementation1/Code7/tree/main/resnet50_adv_pruning
https://github.com/code-implementation1/Code7/tree/main/resnetv2
https://github.com/code-implementation1/Code7/tree/main/resnetv2_50_frn
https://github.com/code-implementation1/Code7/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code8/tree/main/S-GhostNet
https://github.com/code-implementation1/Code8/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code8/tree/main/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/code-implementation1/Code8/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/code-implementation1/Code8/tree/main/squeezenet
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/code-implementation1/Code9/tree/main/Inception
https://github.com/code-implementation1/Code9/tree/main/OCRNet
https://github.com/code-implementation1/Code9/tree/main/ResNet
https://github.com/code-implementation1/Code9/tree/main/SwinTransformer
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/code-implementation1/Code9/tree/main/VIT
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/code-implementation1/Code9/tree/main/regnet
https://github.com/code-implementation1/Code9/tree/main/resnest
https://github.com/code-implementation1/Code9/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/codedecde/Recognizing-Textual-Entailment
https://github.com/codelion/adaptive-classifier
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
https://github.com/codelion/adaptive-classifier/blob/main/src/adaptive_classifier/memory.py
https://github.com/codemonsterphd/pneumollm
Text-guided Foundation Model Adaptation for Pathological Image Classification
https://github.com/cogsys-tuebingen/uninas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/cogtoolslab/physics-benchmarking-neurips2021
Training data-efficient image transformers & distillation through attention
https://github.com/colesturza/CSCI4622-Project
Music Genre Classification with Paralleling Recurrent Convolutional Neural Network
https://github.com/common-english/bert-all
https://github.com/compSciKai/CMPT414-Term-Project-Tensorflow-OCR
https://github.com/computationalpathologygroup/hvit
https://github.com/computerhistory/AlexNet-Source-Code
https://github.com/comramona/neural-statistician
https://github.com/conan7882/adversarial-autoencoders
https://github.com/conan7882/recurrent-attention-model
https://github.com/conan7882/tf-gans
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/conceptofmind/CaiT-Flax
https://github.com/conceptofmind/CrossViT-flax
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
https://github.com/conceptofmind/Crossformer-flax
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.