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Bump slackapi/slack-github-action from 1.23.0 to 1.24.0 (#4066)

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Glenn Jocher 2023-07-31 16:59:21 +02:00 committed by GitHub
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2
.github/workflows/docker.yaml vendored
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@ -86,7 +86,7 @@ jobs:
- name: Notify on failure
if: github.event_name == 'push' && (cancelled() || failure())
uses: slackapi/slack-github-action@v1.23.0
uses: slackapi/slack-github-action@v1.24.0
with:
payload: |
{"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"}

2
docs/datasets/detect/coco.md
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@ -21,7 +21,7 @@ The COCO dataset is split into three subsets:
1. **Train2017**: This subset contains 118K images for training object detection, segmentation, and captioning models.
2. **Val2017**: This subset has 5K images used for validation purposes during model training.
3. **Test2017**: This subset consists of 20K images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://competitions.codalab.org/competitions/5181) for performance evaluation.
3. **Test2017**: This subset consists of 20K images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
## Applications

2
docs/datasets/pose/coco.md
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@ -22,7 +22,7 @@ The COCO-Pose dataset is split into three subsets:
1. **Train2017**: This subset contains a portion of the 118K images from the COCO dataset, annotated for training pose estimation models.
2. **Val2017**: This subset has a selection of images used for validation purposes during model training.
3. **Test2017**: This subset consists of images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://competitions.codalab.org/competitions/5181) for performance evaluation.
3. **Test2017**: This subset consists of images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
## Applications

2
docs/datasets/segment/coco.md
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@ -21,7 +21,7 @@ The COCO-Seg dataset is partitioned into three subsets:
1. **Train2017**: This subset contains 118K images for training instance segmentation models.
2. **Val2017**: This subset includes 5K images used for validation purposes during model training.
3. **Test2017**: This subset encompasses 20K images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://competitions.codalab.org/competitions/5181) for performance evaluation.
3. **Test2017**: This subset encompasses 20K images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7383) for performance evaluation.
## Applications