Semantic Segmentation on CamVid VGG16-encoder (test)
86.03Global AccuracyHN pre-training
Evaluation results
| Method | Links | |
|---|---|---|
| HN pre-trainingArchitecture=SegNet, Encoder=VGG16, Epochs=1002020.02 | 86.03 | |
| ImageNet pre-trainArchitecture=SegNet, Encoder=VGG16, Epochs=1002020.02 | 85.87 | |
| HN pre-trainingArchitecture=SegNet, Encoder=VGG16, Epochs=502020.02 | 84.5 | |
| HN pre-trainingArchitecture=SegNet, Encoder=VGG16, Epochs=102020.02 | 81.7 | |
| HN pre-trainingArchitecture=SegNet, Encoder=VGG16, Epochs=52020.02 | 78.98 | |
| No pre-trainingArchitecture=SegNet, Encoder=VGG16, Epochs=1002020.02 | 71.07 | |
| No pre-trainingArchitecture=SegNet, Encoder=VGG16, Epochs=502020.02 | 54.85 | |
| ImageNet pre-trainArchitecture=SegNet, Encoder=VGG16, Epochs=502020.02 | 42.4 | |
| No pre-trainingArchitecture=SegNet, Encoder=VGG16, Epochs=102020.02 | 31.95 | |
| ImageNet pre-trainArchitecture=SegNet, Encoder=VGG16, Epochs=102020.02 | 27.1 | |
| ImageNet pre-trainArchitecture=SegNet, Encoder=VGG16, Epochs=52020.02 | 24.97 | |
| CIFAR100 pre-trainArchitecture=SegNet, Encoder=VGG16, Epochs=1002020.02 | 15.65 | |
| No pre-trainingArchitecture=SegNet, Encoder=VGG16, Epochs=52020.02 | 10.69 | |
| CIFAR100 pre-trainArchitecture=SegNet, Encoder=VGG16, Epochs=102020.02 | 9.98 | |
| CIFAR100 pre-trainArchitecture=SegNet, Encoder=VGG16, Epochs=52020.02 | 3.54 | |
| CIFAR100 pre-trainArchitecture=SegNet, Encoder=VGG16, Epochs=502020.02 | 2.58 |