Fine Grained Image Classification On Cub 200 1

Accuracy

评测结果

各个模型在此基准测试上的表现结果

比较表格

模型名称	Accuracy
three-branch-and-mutil-scale-learning-for	89.6
feature-boosting-suppression-and	89.5
towards-faster-training-of-global-covariance	88.7
revisiting-weakly-supervised-pre-training-of	91.7
are-these-birds-similar-learning-branched	87.5
learning-multi-attention-convolutional-neural	86.5
learning-a-discriminative-filter-bank-within	87.4
pairwise-confusion-for-fine-grained-visual	86.9
looking-for-the-devil-in-the-details-learning	87.9
align-yourself-self-supervised-pre-training	77.1
context-aware-attentional-pooling-cap-for	91.8
towards-class-specific-unit	88.5
domain-adaptive-transfer-learning-on-visual	91.2
look-into-object-self-supervised-structure	88.0
counterfactual-attention-learning-for-fine	90.6
advisingnets-learning-to-distinguish-correct	88.59
unlabeled-samples-generated-by-gan-improve	84.4
feature-fusion-vision-transformer-fine	91.6
see-better-before-looking-closer-weakly	89.4
high-order-interaction-for-weakly-supervised	90.02%
fixing-the-train-test-resolution-discrepancy	88.7
deformable-part-descriptors-for-fine-grained	50.98
a-novel-plug-in-module-for-fine-grained-1	92.8
fine-grained-visual-classification-with-high-1	93.1%
a-simple-episodic-linear-probe-improves	88.8
transfg-a-transformer-architecture-for-fine	91.7