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Table 2 Evaluation of Ffirst on available leaf datasets: Austrian Federal Forests, Flavia, Foliage, Swedish, Middle European Woods and Leafsnap

From: Fine-grained recognition of plants from images

  AFF Flavia \(10\times 40\) Flavia \(\frac{1}{2}\times \frac{1}{2}\) Foliage Swedish MEW Leafsnap Leafsnap top 5
Num. of classes 5 32 32 60 15 153 185 185
\(\hbox {Ffirst}_\text {a}^{\forall +}\) (1) \(97.1\pm 1.5\) \(99.4\pm 0.3\) \(99.2\pm 0.2\) 99.2 \(99.7\pm 0.3\) \(98.8\pm 0.2\) \(81.2\pm 1.8\) \(95.9\pm 1.5\)
\(\hbox {Ffirst}_\text {i}^{\forall +}\) (2) \(97.3\pm 1.6\) \(99.3\pm 0.3\) \(98.9\pm 0.3\) 98.1 \(99.7\pm 0.3\) \(98.4\pm 0.2\) \(73.1\pm 2.3\) \(92.4\pm 1.7\)
\(\hbox {Ffirst}_\text {b}^{\forall +}\) (3) \(99.5\pm 0.6\) \(99.3\pm 0.4\) \(99.0\pm 0.2\) 98.3 \(99.4\pm 0.5\) \(97.9\pm 0.2\) \(77.2\pm 1.9\) \(94.8\pm 1.5\)
\(\hbox {Ffirst}_{ib\sum }^{\forall +}\) (4) \(100.0\pm 0.0\) \(99.7\pm 0.3\) \(99.6\pm 0.1\) 99.3 \(99.8\pm 0.2\) \(99.3\pm 0.1\) \(81.8\pm 1.2\) \(96.5\pm 1.1\)
\(Ffirst _{ib\prod }^{\forall +}\) (5) \(100.0 \pm 0.0\) \(99.8 \pm 0.3\) 99.7 ± 0.1 99.3 \(99.8 \pm 0.3\) \(99.5 \pm 0.1\) \(83.7 \pm 1.1\) \(97.3 \pm 1.1\)
Inception-ResNet-v2 +maxout \(-\) \(-\) \(-\) \(-\) \(-\) 99.9+ \(-\) \(-\)
Kumar et al. [12] \(-\) \(-\) \(-\) \(-\) \(-\) \(-\) \(\approx\) 73 96.8
Fiel, Sablatnig [11] 93.6 \(-\) \(-\) \(-\) \(-\) \(-\) \(-\) \(-\)
Novotný, Suk [22] \(-\) \(-\) 91.5 \(-\) \(-\) 84.9 \(-\) \(-\)
Karuna et al. [23] \(-\) \(-\) 96.5 \(-\) \(-\) \(-\) \(-\) \(-\)
Kadir et al. [18] \(-\) 95.0 \(-\) 95.8 \(-\) \(-\) \(-\) \(-\)
Lee et al. [21] \(-\) 97.2 \(-\) \(-\) \(-\) \(-\) \(-\) \(-\)
Qi et al. [27] \(-\) \(-\) \(-\) \(-\) 99.4 \(-\) \(-\) \(-\)