Optimize Docs images (#15900)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/datasets/pose/coco.md

@@ -8,7 +8,7 @@ keywords: COCO-Pose, pose estimation, dataset, keypoints, COCO Keypoints 2017, Y
 
 The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset, designed for pose estimation tasks. It leverages the COCO Keypoints 2017 images and labels to enable the training of models like YOLO for pose estimation tasks.
 
-![Pose sample image](https://user-images.githubusercontent.com/26833433/277141128-cd62d09e-1eb0-4d20-9938-c55239a5cb76.jpg)
+![Pose sample image](https://github.com/ultralytics/docs/releases/download/0/pose-sample-image.avif)
 
 ## COCO-Pose Pretrained Models
 
@@ -78,7 +78,7 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 epochs with an im
 
 The COCO-Pose dataset contains a diverse set of images with human figures annotated with keypoints. Here are some examples of images from the dataset, along with their corresponding annotations:
 
-![Dataset sample image](https://user-images.githubusercontent.com/26833433/239690150-a9dc0bd0-7ad9-4b78-a30f-189ed727ea0e.jpg)
+![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-6.avif)
 
 - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
 

docs/en/datasets/pose/coco8-pose.md

@@ -51,7 +51,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
 
 Here are some examples of images from the COCO8-Pose dataset, along with their corresponding annotations:
 
-<img src="https://user-images.githubusercontent.com/26833433/236818283-52eecb96-fc6a-420d-8a26-d488b352dd4c.jpg" alt="Dataset sample image" width="800">
+<img src="https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-5.avif" alt="Dataset sample image" width="800">
 
 - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
 

docs/en/datasets/pose/tiger-pose.md

@@ -64,7 +64,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
 
 Here are some examples of images from the Tiger-Pose dataset, along with their corresponding annotations:
 
-<img src="https://user-images.githubusercontent.com/62513924/272491921-c963d2bf-505f-4a15-abd7-259de302cffa.jpg" alt="Dataset sample image" width="100%">
+<img src="https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-4.avif" alt="Dataset sample image" width="100%">
 
 - **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.