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Update image count information for COCO-Pose (#18395)

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Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Mohammed Yasin 2024-12-26 02:33:15 +08:00 • committed by GitHub
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docs/en/datasets/pose/coco.md
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@ -24,8 +24,8 @@ The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialize
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.
1. **Train2017**: This subset contains 56599 images from the COCO dataset, annotated for training pose estimation models.
2. **Val2017**: This subset has 2346 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://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
## Applications
@ -139,9 +139,9 @@ The COCO-Pose dataset provides several standardized evaluation metrics for pose
The COCO-Pose dataset is split into three subsets:
1. **Train2017**: Contains a portion of the 118K COCO images, annotated for training pose estimation models.
2. **Val2017**: Selected images for validation purposes during model training.
3. **Test2017**: Images used for testing and benchmarking trained models. Ground truth annotations for this subset are not publicly available; results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
1. **Train2017**: Contains 56599 COCO images, annotated for training pose estimation models.
2. **Val2017**: 2346 images for validation purposes during model training.
3. **Test2017**: Images used for testing and benchmarking trained models. Ground truth annotations for this subset are not publicly available; results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7403) for performance evaluation.
These subsets help organize the training, validation, and testing phases effectively. For configuration details, explore the `coco-pose.yaml` file available on [GitHub](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml).

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ultralytics/cfg/datasets/coco-pose.yaml
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@ -1,5 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
# COCO 2017 dataset https://cocodataset.org by Microsoft
# COCO 2017 Keypoints dataset https://cocodataset.org by Microsoft
# Documentation: https://docs.ultralytics.com/datasets/pose/coco/
# Example usage: yolo train data=coco-pose.yaml
# parent
@ -9,9 +9,9 @@
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco-pose # dataset root dir
train: train2017.txt # train images (relative to 'path') 118287 images
val: val2017.txt # val images (relative to 'path') 5000 images
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
train: train2017.txt # train images (relative to 'path') 56599 images
val: val2017.txt # val images (relative to 'path') 2346 images
test: test-dev2017.txt # 20288 of 40670 images, submit to https://codalab.lisn.upsaclay.fr/competitions/7403
# Keypoints
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)