2D Object Detection On Sardet 100K
Metrics
box mAP
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | box mAP |
|---|---|
| sardet-100k-towards-open-source-benchmark-and | 51.1 |
| cascade-r-cnn-delving-into-high-quality | 51.1 |
| fcos-fully-convolutional-one-stage-object | 49.8 |
| sardet-100k-towards-open-source-benchmark-and | 54.8 |
| denodet-attention-as-deformable-multi | 55.4 |
| grid-r-cnn | 48.8 |
| sparse-r-cnn-end-to-end-object-detection-with | 38.1 |
| saratr-x-a-foundation-model-for-synthetic | - |
| deformable-detr-deformable-transformers-for-1 | 50.0 |
| sardet-100k-towards-open-source-benchmark-and | 53.7 |
| focal-loss-for-dense-object-detection | 47.4 |
| faster-r-cnn-towards-real-time-object | 49.0 |
| sardet-100k-towards-open-source-benchmark-and | 51.3 |