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Object Detection On Cppe 5

Metrics

AP50
AP75
APL
APM
APS
box AP

Results

Performance results of various models on this benchmark

Model Name
AP50
AP75
APL
APM
APS
box AP
Paper TitleRepository
Double Heads87.355.260.841.038.652.0CPPE-5: Medical Personal Protective Equipment Dataset-
YOLOv379.435.349.028.423.138.5CPPE-5: Medical Personal Protective Equipment Dataset-
Sparse RCNN69.644.654.730.630.044.0CPPE-5: Medical Personal Protective Equipment Dataset-
Deformable DETR76.952.853.935.236.448.0CPPE-5: Medical Personal Protective Equipment Dataset-
RegNet85.351.860.541.135.751.3CPPE-5: Medical Personal Protective Equipment Dataset-
TridentNet85.158.362.641.342.652.9CPPE-5: Medical Personal Protective Equipment Dataset-
FCOS79.545.951.739.236.744.4CPPE-5: Medical Personal Protective Equipment Dataset-
RepPoints75.940.148.036.727.343.0CPPE-5: Medical Personal Protective Equipment Dataset-
VarifocalNet82.656.758.842.139.051.0CPPE-5: Medical Personal Protective Equipment Dataset-
Empirical Attention86.554.161.043.438.752.5CPPE-5: Medical Personal Protective Equipment Dataset-
Deformable Convolutional Network87.155.961.341.436.351.6CPPE-5: Medical Personal Protective Equipment Dataset-
Faster RCNN73.847.852.534.730.044.0CPPE-5: Medical Personal Protective Equipment Dataset-
Grid RCNN77.950.654.437.243.447.5CPPE-5: Medical Personal Protective Equipment Dataset-
Localization Distillation76.558.859.443.045.850.9CPPE-5: Medical Personal Protective Equipment Dataset-
FSAF84.748.256.739.645.349.2CPPE-5: Medical Personal Protective Equipment Dataset-
SSD57.024.934.623.132.129.50CPPE-5: Medical Personal Protective Equipment Dataset-
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Object Detection On Cppe 5 | SOTA | HyperAI