diff --git a/.github/workflows/docker.yaml b/.github/workflows/docker.yaml
index eb78e9a..bd4d002 100644
--- a/.github/workflows/docker.yaml
+++ b/.github/workflows/docker.yaml
@@ -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"}
diff --git a/docs/datasets/detect/coco.md b/docs/datasets/detect/coco.md
index 9406869..b8d1934 100644
--- a/docs/datasets/detect/coco.md
+++ b/docs/datasets/detect/coco.md
@@ -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
 
diff --git a/docs/datasets/pose/coco.md b/docs/datasets/pose/coco.md
index 1549fd3..715cad8 100644
--- a/docs/datasets/pose/coco.md
+++ b/docs/datasets/pose/coco.md
@@ -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
 
diff --git a/docs/datasets/segment/coco.md b/docs/datasets/segment/coco.md
index 2738703..1f144c2 100644
--- a/docs/datasets/segment/coco.md
+++ b/docs/datasets/segment/coco.md
@@ -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