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/fastforwardlabs/causality-for-ml
https://github.com/fastforwardlabs/deepad
https://github.com/fastforwardlabs/learning-to-learn
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/fastscience-ai/medflamingo
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/fatLime/Predict-Lung-Disease
https://github.com/faustomorales/vit-keras
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/fbuchert/fixmatch-pytorch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/fbuchert/mixmatch-pytorch
https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/fchollet/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/federicopozzi33/MobileOne-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/feidfoe/AdjustBnd4Imbalance
https://github.com/felixgwu/img_classification_pk_pytorch
https://github.com/felixriese/CNN-SoilTextureClassification
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
https://github.com/fengjiqiang/pretrainedmodel_pytorch
https://github.com/fenglian425/Agriculture_AI
https://github.com/fengredrum/cnn-xla
https://github.com/fengye-lu/PyTorch-SRGAN
https://github.com/fer-moreira/ColorizerBot
https://github.com/ferna11i/detectron2_ResNeST
https://github.com/fescobar96/Image-Noise-Removal
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/few-shot-NER-benchmark/BaselineCode
https://github.com/fhvilshoj/ecinn
ECINN: Efficient Counterfactuals from Invertible Neural Networks
https://github.com/fiabdu/Commonly-Interesting-Images
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/fidler-lab/efficient-annotation-cookbook
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets
https://github.com/filaPro/visda2019
https://github.com/filipbasara0/matryoshka-representation-learning
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/filipbasara0/relic
https://github.com/filipbasara0/simple-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/filipmu/Kaggle-APTOS-2019-Blindness
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/firja10/darknet-2
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/firojalam/multimodal_social_media
Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response
https://github.com/fishba11/food-1010keras
https://github.com/fitushar/3D-Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/fiveai/on-episodes-fsl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.