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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/pranavphoenix/cxv
공식 코드https://github.com/prasunroy/cnn-on-degraded-images
공식 코드Effects of Degradations on Deep Neural Network Architectures
https://github.com/prateekmunjal/torchal
공식 코드Towards Robust and Reproducible Active Learning Using Neural Networks
https://github.com/pratikkayal/PlantDoc-Dataset
공식 코드https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset
공식 코드https://github.com/praveenchopra/ProgressiveSpinalNet
공식 코드https://github.com/predictive-analytics-lab/nifr
공식 코드https://github.com/preetam22n/deeptrans-hsu
공식 코드https://github.com/preetum/deep-bootstrap-code
공식 코드The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers
https://github.com/prijatelj/bayesian_eval_ground_truth-free
공식 코드A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks
https://github.com/princeton-computational-imaging/DirtyPixels
공식 코드Dirty Pixels: Towards End-to-End Image Processing and Perception
https://github.com/princeton-nlp/datamux
공식 코드https://github.com/princetonvisualai/imagenetood
공식 코드ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms
https://github.com/princetonvisualai/multimodal_dataset_distillation
공식 코드https://github.com/pris-cv/afgr
공식 코드https://github.com/pris-cv/bi-frn
공식 코드Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification
https://github.com/pris-cv/making-a-bird-ai-expert-work-for-you-and-me
공식 코드https://github.com/pris-cv/relmatch
공식 코드Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data
https://github.com/pris-nlp/coling2022_bayesian-for-ood
공식 코드Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation
https://github.com/prithuls/mv-swin-t
공식 코드MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer
https://github.com/priyankkalgaonkar/CondenseNeXt
공식 코드CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems
https://github.com/priyankkalgaonkar/Image-Classification-with-CondenseNeXt-for-ARM-Based-Computing-Platforms
공식 코드Image Classification with CondenseNeXt for ARM-Based Computing Platforms
https://github.com/prlz77/attend-and-rectify
공식 코드Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery
https://github.com/probabilistic-and-interactive-ml/breaking-the-reclustering-barrier
공식 코드Breaking the Reclustering Barrier in Centroid-based Deep Clustering
https://github.com/promise-z5q2sq/eeg-imagenet-dataset
공식 코드https://github.com/pronobis/libspn-keras
공식 코드https://github.com/protim1191/glodismo_classifier
공식 코드Compressed learning based onboard semantic compression for remote sensing platforms
https://github.com/prs-eth/marigold
공식 코드Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis
https://github.com/psellcam/LaplaceNet
공식 코드LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification
https://github.com/psellcam/Superpixel-Contracted-Graph-Based-Learning-for-Hyperspectral-Image-Classification
공식 코드Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
https://github.com/pseulki/ib-loss
공식 코드Influence-Balanced Loss for Imbalanced Visual Classification
https://github.com/psinger/kaggle-landmark-recognition-2020-1st-place
공식 코드Supporting large-scale image recognition with out-of-domain samples
https://github.com/psosnin/AbstractGradientTraining
공식 코드Certification for Differentially Private Prediction in Gradient-Based Training
https://github.com/psteinb/sota_on_uncertainties
공식 코드https://github.com/puar-playground/lra-diffusion
공식 코드Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.