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Models/DeeplabV3+

DeeplabV3+

Reported on 180 benchmarks across 6 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology120 results

3DonDIS-TE4
E-measure· 2017-06-17
0.82
best: 0.944 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE4
HCE· 2017-06-17
3709
best: 3999 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE4
MAE· 2017-06-17
0.111
best: 0.037 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE4
S-Measure· 2017-06-17
0.744
best: 0.91 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE4
max F-Measure· 2017-06-17
0.715
best: 0.912 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE4
weighted F-measure· 2017-06-17
0.621
best: 0.867 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-VD
E-measure· 2017-06-17
0.796
best: 0.958 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-VD
HCE· 2017-06-17
1520
best: 1660 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-VD
MAE· 2017-06-17
0.114
best: 0.027 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-VD
S-Measure· 2017-06-17
0.716
best: 0.917 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-VD
max F-Measure· 2017-06-17
0.66
best: 0.923 (BEN_Base)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-VD
weighted F-measure· 2017-06-17
0.568
best: 0.896 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE2
E-measure· 2017-06-17
0.813
best: 0.947 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE2
HCE· 2017-06-17
516
best: 621 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE2
MAE· 2017-06-17
0.105
best: 0.028 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE2
S-Measure· 2017-06-17
0.729
best: 0.924 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE2
max F-Measure· 2017-06-17
0.681
best: 0.921 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE2
weighted F-measure· 2017-06-17
0.587
best: 0.885 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE1
E-measure· 2017-06-17
0.772
best: 0.927 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE1
HCE· 2017-06-17
234
best: 288 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE1
MAE· 2017-06-17
0.102
best: 0.031 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE1
S-Measure· 2017-06-17
0.694
best: 0.899 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE1
max F-Measure· 2017-06-17
0.601
best: 0.89 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE1
weighted F-measure· 2017-06-17
0.506
best: 0.846 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE3
E-measure· 2017-06-17
0.833
best: 0.957 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE3
HCE· 2017-06-17
999
best: 1146 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE3
MAE· 2017-06-17
0.102
best: 0.027 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE3
S-Measure· 2017-06-17
0.749
best: 0.928 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE3
max F-Measure· 2017-06-17
0.717
best: 0.936 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
3DonDIS-TE3
weighted F-measure· 2017-06-17
0.623
best: 0.9 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE4
E-measure· 2017-06-17
0.82
best: 0.944 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE4
HCE· 2017-06-17
3709
best: 3999 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE4
MAE· 2017-06-17
0.111
best: 0.037 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE4
S-Measure· 2017-06-17
0.744
best: 0.91 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE4
max F-Measure· 2017-06-17
0.715
best: 0.912 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE4
weighted F-measure· 2017-06-17
0.621
best: 0.867 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-VD
E-measure· 2017-06-17
0.796
best: 0.958 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-VD
HCE· 2017-06-17
1520
best: 1660 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-VD
MAE· 2017-06-17
0.114
best: 0.027 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-VD
S-Measure· 2017-06-17
0.716
best: 0.917 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-VD
max F-Measure· 2017-06-17
0.66
best: 0.923 (BEN_Base)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-VD
weighted F-measure· 2017-06-17
0.568
best: 0.896 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE2
E-measure· 2017-06-17
0.813
best: 0.947 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE2
HCE· 2017-06-17
516
best: 621 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE2
MAE· 2017-06-17
0.105
best: 0.028 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE2
S-Measure· 2017-06-17
0.729
best: 0.924 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE2
max F-Measure· 2017-06-17
0.681
best: 0.921 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE2
weighted F-measure· 2017-06-17
0.587
best: 0.885 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE1
E-measure· 2017-06-17
0.772
best: 0.927 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE1
HCE· 2017-06-17
234
best: 288 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE1
MAE· 2017-06-17
0.102
best: 0.031 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE1
S-Measure· 2017-06-17
0.694
best: 0.899 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE1
max F-Measure· 2017-06-17
0.601
best: 0.89 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE1
weighted F-measure· 2017-06-17
0.506
best: 0.846 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE3
E-measure· 2017-06-17
0.833
best: 0.957 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE3
HCE· 2017-06-17
999
best: 1146 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE3
MAE· 2017-06-17
0.102
best: 0.027 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE3
S-Measure· 2017-06-17
0.749
best: 0.928 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE3
max F-Measure· 2017-06-17
0.717
best: 0.936 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D ClassificationonDIS-TE3
weighted F-measure· 2017-06-17
0.623
best: 0.9 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE4
E-measure· 2017-06-17
0.82
best: 0.944 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE4
HCE· 2017-06-17
3709
best: 3999 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE4
MAE· 2017-06-17
0.111
best: 0.037 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE4
S-Measure· 2017-06-17
0.744
best: 0.91 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE4
max F-Measure· 2017-06-17
0.715
best: 0.912 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE4
weighted F-measure· 2017-06-17
0.621
best: 0.867 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-VD
E-measure· 2017-06-17
0.796
best: 0.958 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-VD
HCE· 2017-06-17
1520
best: 1660 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-VD
MAE· 2017-06-17
0.114
best: 0.027 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-VD
S-Measure· 2017-06-17
0.716
best: 0.917 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-VD
max F-Measure· 2017-06-17
0.66
best: 0.923 (BEN_Base)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-VD
weighted F-measure· 2017-06-17
0.568
best: 0.896 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE2
E-measure· 2017-06-17
0.813
best: 0.947 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE2
HCE· 2017-06-17
516
best: 621 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE2
MAE· 2017-06-17
0.105
best: 0.028 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE2
S-Measure· 2017-06-17
0.729
best: 0.924 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE2
max F-Measure· 2017-06-17
0.681
best: 0.921 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE2
weighted F-measure· 2017-06-17
0.587
best: 0.885 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE1
E-measure· 2017-06-17
0.772
best: 0.927 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE1
HCE· 2017-06-17
234
best: 288 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE1
MAE· 2017-06-17
0.102
best: 0.031 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE1
S-Measure· 2017-06-17
0.694
best: 0.899 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE1
max F-Measure· 2017-06-17
0.601
best: 0.89 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE1
weighted F-measure· 2017-06-17
0.506
best: 0.846 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE3
E-measure· 2017-06-17
0.833
best: 0.957 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE3
HCE· 2017-06-17
999
best: 1146 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE3
MAE· 2017-06-17
0.102
best: 0.027 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE3
S-Measure· 2017-06-17
0.749
best: 0.928 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE3
max F-Measure· 2017-06-17
0.717
best: 0.936 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
2D Object DetectiononDIS-TE3
weighted F-measure· 2017-06-17
0.623
best: 0.9 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE4
E-measure· 2017-06-17
0.82
best: 0.944 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE4
HCE· 2017-06-17
3709
best: 3999 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE4
MAE· 2017-06-17
0.111
best: 0.037 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE4
S-Measure· 2017-06-17
0.744
best: 0.91 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE4
max F-Measure· 2017-06-17
0.715
best: 0.912 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE4
weighted F-measure· 2017-06-17
0.621
best: 0.867 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-VD
E-measure· 2017-06-17
0.796
best: 0.958 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-VD
HCE· 2017-06-17
1520
best: 1660 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-VD
MAE· 2017-06-17
0.114
best: 0.027 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-VD
S-Measure· 2017-06-17
0.716
best: 0.917 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-VD
max F-Measure· 2017-06-17
0.66
best: 0.923 (BEN_Base)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-VD
weighted F-measure· 2017-06-17
0.568
best: 0.896 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE2
E-measure· 2017-06-17
0.813
best: 0.947 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE2
HCE· 2017-06-17
516
best: 621 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE2
MAE· 2017-06-17
0.105
best: 0.028 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE2
S-Measure· 2017-06-17
0.729
best: 0.924 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE2
max F-Measure· 2017-06-17
0.681
best: 0.921 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE2
weighted F-measure· 2017-06-17
0.587
best: 0.885 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE1
E-measure· 2017-06-17
0.772
best: 0.927 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE1
HCE· 2017-06-17
234
best: 288 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE1
MAE· 2017-06-17
0.102
best: 0.031 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE1
S-Measure· 2017-06-17
0.694
best: 0.899 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE1
max F-Measure· 2017-06-17
0.601
best: 0.89 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE1
weighted F-measure· 2017-06-17
0.506
best: 0.846 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE3
E-measure· 2017-06-17
0.833
best: 0.957 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE3
HCE· 2017-06-17
999
best: 1146 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE3
MAE· 2017-06-17
0.102
best: 0.027 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE3
S-Measure· 2017-06-17
0.749
best: 0.928 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE3
max F-Measure· 2017-06-17
0.717
best: 0.936 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
16konDIS-TE3
weighted F-measure· 2017-06-17
0.623
best: 0.9 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587

Computer Vision60 results

Object DetectiononDIS-TE4
E-measure· 2017-06-17
0.82
best: 0.944 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE4
HCE· 2017-06-17
3709
best: 3999 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE4
MAE· 2017-06-17
0.111
best: 0.037 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE4
S-Measure· 2017-06-17
0.744
best: 0.91 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE4
max F-Measure· 2017-06-17
0.715
best: 0.912 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE4
weighted F-measure· 2017-06-17
0.621
best: 0.867 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-VD
E-measure· 2017-06-17
0.796
best: 0.958 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-VD
HCE· 2017-06-17
1520
best: 1660 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-VD
MAE· 2017-06-17
0.114
best: 0.027 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-VD
S-Measure· 2017-06-17
0.716
best: 0.917 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-VD
max F-Measure· 2017-06-17
0.66
best: 0.923 (BEN_Base)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-VD
weighted F-measure· 2017-06-17
0.568
best: 0.896 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE2
E-measure· 2017-06-17
0.813
best: 0.947 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE2
HCE· 2017-06-17
516
best: 621 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE2
MAE· 2017-06-17
0.105
best: 0.028 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE2
S-Measure· 2017-06-17
0.729
best: 0.924 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE2
max F-Measure· 2017-06-17
0.681
best: 0.921 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE2
weighted F-measure· 2017-06-17
0.587
best: 0.885 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE1
E-measure· 2017-06-17
0.772
best: 0.927 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE1
HCE· 2017-06-17
234
best: 288 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE1
MAE· 2017-06-17
0.102
best: 0.031 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE1
S-Measure· 2017-06-17
0.694
best: 0.899 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE1
max F-Measure· 2017-06-17
0.601
best: 0.89 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE1
weighted F-measure· 2017-06-17
0.506
best: 0.846 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE3
E-measure· 2017-06-17
0.833
best: 0.957 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE3
HCE· 2017-06-17
999
best: 1146 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE3
MAE· 2017-06-17
0.102
best: 0.027 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE3
S-Measure· 2017-06-17
0.749
best: 0.928 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE3
max F-Measure· 2017-06-17
0.717
best: 0.936 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
Object DetectiononDIS-TE3
weighted F-measure· 2017-06-17
0.623
best: 0.9 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE4
E-measure· 2017-06-17
0.82
best: 0.944 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE4
HCE· 2017-06-17
3709
best: 3999 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE4
MAE· 2017-06-17
0.111
best: 0.037 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE4
S-Measure· 2017-06-17
0.744
best: 0.91 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE4
max F-Measure· 2017-06-17
0.715
best: 0.912 (MVANet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE4
weighted F-measure· 2017-06-17
0.621
best: 0.867 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-VD
E-measure· 2017-06-17
0.796
best: 0.958 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-VD
HCE· 2017-06-17
1520
best: 1660 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-VD
MAE· 2017-06-17
0.114
best: 0.027 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-VD
S-Measure· 2017-06-17
0.716
best: 0.917 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-VD
max F-Measure· 2017-06-17
0.66
best: 0.923 (BEN_Base)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-VD
weighted F-measure· 2017-06-17
0.568
best: 0.896 (BEN_Base+Refiner)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE2
E-measure· 2017-06-17
0.813
best: 0.947 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE2
HCE· 2017-06-17
516
best: 621 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE2
MAE· 2017-06-17
0.105
best: 0.028 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE2
S-Measure· 2017-06-17
0.729
best: 0.924 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE2
max F-Measure· 2017-06-17
0.681
best: 0.921 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE2
weighted F-measure· 2017-06-17
0.587
best: 0.885 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE1
E-measure· 2017-06-17
0.772
best: 0.927 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE1
HCE· 2017-06-17
234
best: 288 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE1
MAE· 2017-06-17
0.102
best: 0.031 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE1
S-Measure· 2017-06-17
0.694
best: 0.899 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE1
max F-Measure· 2017-06-17
0.601
best: 0.89 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE1
weighted F-measure· 2017-06-17
0.506
best: 0.846 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE3
E-measure· 2017-06-17
0.833
best: 0.957 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE3
HCE· 2017-06-17
999
best: 1146 (BSV1)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE3
MAE· 2017-06-17
0.102
best: 0.027 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE3
S-Measure· 2017-06-17
0.749
best: 0.928 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE3
max F-Measure· 2017-06-17
0.717
best: 0.936 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587
RGB Salient Object DetectiononDIS-TE3
weighted F-measure· 2017-06-17
0.623
best: 0.9 (PDFNet)
Rethinking Atrous Convolution for Semantic Image Segmentation arXiv:1706.05587