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Models/VGG-19

VGG-19

Reported on 10 benchmarks across 3 tasks · 2 papers · 1 SOTA

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

Computer Vision7 results

Parking Space OccupancyonPKLot
F1-score· 2023-06-07
0.9988
SOTA
Revising deep learning methods in parking lot occupancy detection arXiv:2306.04288
Parking Space OccupancyonSPKL
F1-score· 2023-06-07
0.6801
best: 0.7393 (EfficientNet-P)
Revising deep learning methods in parking lot occupancy detection arXiv:2306.04288
Parking Space OccupancyonAction-Camera Parking
F1-score· 2023-06-07
0.9152
best: 0.9343 (MobileNetV2)
Revising deep learning methods in parking lot occupancy detection arXiv:2306.04288
Parking Space OccupancyonCNRPark+EXT
F1-score· 2023-06-07
0.9629
best: 0.9683 (EfficientNet-P)
Revising deep learning methods in parking lot occupancy detection arXiv:2306.04288
Domain GeneralizationonVizWiz-Classification
Accuracy - All Images· 2014-09-04
34.7
best: 57.2 (VOLO-D5)
Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556
Domain GeneralizationonVizWiz-Classification
Accuracy - Clean Images· 2014-09-04
39.3
best: 450 (ViT-8/B-224)
Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556
Domain GeneralizationonVizWiz-Classification
Accuracy - Corrupted Images· 2014-09-04
29
best: 51.8 (VOLO-D5)
Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556

Methodology3 results

Domain AdaptationonVizWiz-Classification
Accuracy - All Images· 2014-09-04
34.7
best: 57.2 (VOLO-D5)
Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556
Domain AdaptationonVizWiz-Classification
Accuracy - Clean Images· 2014-09-04
39.3
best: 450 (ViT-8/B-224)
Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556
Domain AdaptationonVizWiz-Classification
Accuracy - Corrupted Images· 2014-09-04
29
best: 51.8 (VOLO-D5)
Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556