Lane Detection On Tusimple
評価指標
Accuracy
F1 score
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | Accuracy | F1 score |
|---|---|---|
| keep-your-eyes-on-the-lane-attention-guided | 95.57% | 96.71 |
| clrnet-cross-layer-refinement-network-for | - | 97.62 |
| rethinking-efficient-lane-detection-via-curve | 95.65% | - |
| el-gan-embedding-loss-driven-generative | 96.40% | 96.26 |
| rethinking-efficient-lane-detection-via-curve | 95.41% | - |
| 190503704 | 96.29% | 95.23 |
| keep-your-eyes-on-the-lane-attention-guided | 96.10% | 96.06 |
| learning-to-cluster-for-proposal-free | 96.50% | 94.31 |
| eigenlanes-data-driven-lane-descriptors-for | 95.62% | - |
| end-to-end-lane-marker-detection-via-row-wise | 96.22% | 96.58 |
| condlanenet-a-top-to-down-lane-detection | 95.37% | 96.98 |
| canet-curved-guide-line-network-with-adaptive | 96.76% | 97.77 |
| laneaf-robust-multi-lane-detection-with | 95.64% | 96.49 |
| robust-lane-detection-through-self-pre | 98.38 | - |
| focus-on-local-detecting-lane-marker-from | 96.92 | - |
| lane-detection-and-classification-using | 95.24% | 90.82 |
| モデル 17 | 94.5% | - |
| a-keypoint-based-global-association-network | - | 97.68 |
| contrastive-learning-for-lane-detection-via | 96.82 | - |
| canet-curved-guide-line-network-with-adaptive | 96.56% | 97.51 |
| towards-lightweight-lane-detection-by | 96.58% | 96.38 |
| polylanenet-lane-estimation-via-deep | 93.36% | 90.62 |
| condlanenet-a-top-to-down-lane-detection | 95.48% | - |
| end-to-end-lane-shape-prediction-with | 96.18 | 96.68 |
| learning-lightweight-lane-detection-cnns-by | 96.64% | 95.92 |
| a-keypoint-based-global-association-network | - | 97.71 |
| lane-detection-with-position-embedding | 96.93 | - |
| condlanenet-a-top-to-down-lane-detection | - | 97.01 |
| モデル 29 | 96.50% | 97.42 |
| モデル 30 | 95.84% | - |
| towards-end-to-end-lane-detection-an-instance | 96.4% | 94.80 |
| keep-your-eyes-on-the-lane-attention-guided | 95.63% | 96.77 |
| canet-curved-guide-line-network-with-adaptive | 96.66% | 97.44 |
| semantic-instance-segmentation-with-a | 96.40% | - |
| condlanenet-a-top-to-down-lane-detection | 96.54% | 97.24 |
| end-to-end-lane-marker-detection-via-row-wise | 96.11% | 96.37 |
| clrnet-cross-layer-refinement-network-for | 96.82% | 97.89 |
| clrnet-cross-layer-refinement-network-for | 96.9% | 97.82 |
| a-keypoint-based-global-association-network | - | 97.45 |
| end-to-end-lane-marker-detection-via-row-wise | 96.02% | 96.25 |
| resa-recurrent-feature-shift-aggregator-for | 96.82 | 96.93 |