diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 527ae5c3f..444dcbd38 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -19,6 +19,10 @@ on: - 'examples/**' - '.dev_scripts/**' +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: build_cpu: runs-on: ubuntu-latest diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index bed56b823..b2981c8e0 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -2,6 +2,10 @@ name: lint on: [push, pull_request] +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: lint: runs-on: ubuntu-latest diff --git a/configs/sot/siamese_rpn/metafile.yml b/configs/sot/siamese_rpn/metafile.yml index 0a43224c3..8efc4671f 100644 --- a/configs/sot/siamese_rpn/metafile.yml +++ b/configs/sot/siamese_rpn/metafile.yml @@ -24,9 +24,10 @@ Models: - Task: Single Object Tracking Dataset: LaSOT Metrics: - Success: 49.9 - Norm precision: 57.9 - Weights: https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_lasot/siamese_rpn_r50_1x_lasot_20201218_051019-3c522eff.pth + Success: 50.1 + Norm precision: 59.1 + Precision: 48.7 + Weights: https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_lasot/siamese_rpn_r50_1x_lasot_20211203_151612-da4b3c66.pth - Name: siamese_rpn_r50_1x_uav123 In Collection: SiameseRPN++ @@ -39,10 +40,10 @@ Models: - Task: Single Object Tracking Dataset: UAV123 Metrics: - Success: 60.6 - Norm precision: 76.5 - Precision: 80.5 - Weights: https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_uav123/siamese_rpn_r50_1x_uav123_20210917_104452-36ac4934.pth + Success: 59.8 + Norm precision: 77.3 + Precision: 80.0 + Weights: https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_uav123/siamese_rpn_r50_1x_uav123_20211203_153949-6d82f39e.pth - Name: siamese_rpn_r50_1x_trackingnet In Collection: SiameseRPN++ @@ -55,10 +56,10 @@ Models: - Task: Single Object Tracking Dataset: TrackingNet Metrics: - Success: 70.6 - Norm precision: 77.6 - Precision: 65.7 - Weights: https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_trackingnet/siamese_rpn_r50_1x_lasot_20201218_051019-3c522eff.pth + Success: 69.0 + Norm precision: 75.8 + Precision: 63.2 + Weights: https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_lasot/siamese_rpn_r50_1x_lasot_20211203_151612-da4b3c66.pth - Name: siamese_rpn_r50_1x_otb100 In Collection: SiameseRPN++ @@ -71,10 +72,26 @@ Models: - Task: Single Object Tracking Dataset: OTB100 Metrics: - Success: 64.8 - Norm precision: 83.2 - Precision: 87.7 - Weights: https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_otb100/siamese_rpn_r50_1x_otb100_20210920_001757-12636a0a.pth + Success: 65.1 + Norm precision: 82.0 + Precision: 86.1 + Weights: https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_otb100/siamese_rpn_r50_1x_otb100_20211203_154048-9dfde4fa.pth + + - Name: siamese_rpn_r50_1x_vot2018 + In Collection: SiameseRPN++ + Config: configs/sot/siamese_rpn/siamese_rpn_r50_1x_vot2018.py + Metadata: + Training Data: MSCOCO, ImageNet DET, ImageNet VID + Training Memory (GB): _ + Epochs: 20 + Results: + - Task: Single Object Tracking + Dataset: VOT2018 + Metrics: + EAO: 0.348 + Accuracy: 0.578 + Robustness: 0.272 + Weights: https://download.openmmlab.com/mmtracking/sot/siamese_rpn/siamese_rpn_r50_1x_vot2018/siamese_rpn_r50_1x_vot2018_20211206_211710-10e082cd.pth - Name: siamese_rpn_r50_fp16_1x_lasot In Collection: SiameseRPN++