From 3e65fc24217b8d08ec27039ae41413afe7b8465c Mon Sep 17 00:00:00 2001 From: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Date: Thu, 26 Dec 2024 02:33:15 +0800 Subject: [PATCH] Update image count information for COCO-Pose (#18395) Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Co-authored-by: Glenn Jocher --- docs/en/datasets/pose/coco.md | 10 +++++----- ultralytics/cfg/datasets/coco-pose.yaml | 8 ++++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/en/datasets/pose/coco.md b/docs/en/datasets/pose/coco.md index 22adca3b..81226c77 100644 --- a/docs/en/datasets/pose/coco.md +++ b/docs/en/datasets/pose/coco.md @@ -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). diff --git a/ultralytics/cfg/datasets/coco-pose.yaml b/ultralytics/cfg/datasets/coco-pose.yaml index 7d71c83d..9a483072 100644 --- a/ultralytics/cfg/datasets/coco-pose.yaml +++ b/ultralytics/cfg/datasets/coco-pose.yaml @@ -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)