Accuracy: 99.26
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Fine-Grained Image Classification
Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.
<span style="color:grey; opacity: 0.6">( Image credit: Looking for the Devil in the Details )</span>
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
벤치마크
Papers with Code 아카이브의 데이터셋별 기록 예시입니다. 평가 조건과 지표 방향이 검증되지 않아 순위를 의미하지 않습니다.
Dataset기록된 모델 예시MetricPaperCode
데이터셋
36개논문
Accuracy: 88.3%
Accuracy: 91.6
Accuracy: 91.6%
Accuracy: 88.59
Accuracy: 95.00
Accuracy: 95.86
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition
Accuracy: 91.1%
Accuracy: 91.7
Accuracy: 91.3%
라이브러리
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