Accuracy: 93.50
Papers with Code · CC BY-SA 4.0
Few-Shot Image Classification
Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically < 6 examples). The goal is to enable models to recognize and classify new images with minimal supervision and limited data, without having to train on large datasets. (typically < 6 examples)
<span style="color:grey; opacity: 0.6">( Image credit: Learning Embedding Adaptation for Few-Shot Learning )</span>
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벤치마크
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Dataset기록된 모델 예시MetricPaperCode
데이터셋
117개논문
Accuracy: 98.7
Accuracy: 93.5
Accuracy: 94.02
Accuracy: 92.48
Accuracy: 93.99
Accuracy: 96.28
Accuracy: 96.43
Accuracy: 93.37
Accuracy: 97.12
Accuracy: 97.12
Accuracy: 94.09
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.
github.com/yhu01/bms
2 논문