diff --git a/docs/en/datasets/segment/carparts-seg.md b/docs/en/datasets/segment/carparts-seg.md index 96615752..dbc69ce4 100644 --- a/docs/en/datasets/segment/carparts-seg.md +++ b/docs/en/datasets/segment/carparts-seg.md @@ -12,13 +12,13 @@ Whether you're working on automotive research, developing AI solutions for vehic


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- Watch: Carparts Instance Segmentation Using Ultralytics HUB + Watch: Carparts Instance Segmentation with Ultralytics YOLO11

## Dataset Structure diff --git a/docs/en/guides/model-evaluation-insights.md b/docs/en/guides/model-evaluation-insights.md index 24514f4a..5bd8bede 100644 --- a/docs/en/guides/model-evaluation-insights.md +++ b/docs/en/guides/model-evaluation-insights.md @@ -10,6 +10,17 @@ keywords: Model Evaluation, Machine Learning Model Evaluation, Fine Tuning Machi Once you've [trained](./model-training-tips.md) your computer vision model, evaluating and refining it to perform optimally is essential. Just training your model isn't enough. You need to make sure that your model is accurate, efficient, and fulfills the [objective](./defining-project-goals.md) of your computer vision project. By evaluating and fine-tuning your model, you can identify weaknesses, improve its accuracy, and boost overall performance. +

+
+ +
+ Watch: Insights into Model Evaluation and Fine-Tuning | Tips for Improving Mean Average Precision +

+ In this guide, we'll share insights on model evaluation and fine-tuning that'll make this [step of a computer vision project](./steps-of-a-cv-project.md) more approachable. We'll discuss how to understand evaluation metrics and implement fine-tuning techniques, giving you the knowledge to elevate your model's capabilities. ## Evaluating Model Performance Using Metrics diff --git a/docs/en/guides/region-counting.md b/docs/en/guides/region-counting.md index d1c439fa..d2c9a55a 100644 --- a/docs/en/guides/region-counting.md +++ b/docs/en/guides/region-counting.md @@ -12,13 +12,13 @@ keywords: object counting, regions, YOLOv8, computer vision, Ultralytics, effici


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- Watch: Ultralytics YOLOv8 Object Counting in Multiple & Movable Regions + Watch: Object Counting in Different Regions using Ultralytics YOLO11 | Ultralytics Solutions 🚀

## Advantages of Object Counting in Regions? diff --git a/docs/en/models/sam-2.md b/docs/en/models/sam-2.md index 983d8cdc..8d39b5ea 100644 --- a/docs/en/models/sam-2.md +++ b/docs/en/models/sam-2.md @@ -271,6 +271,17 @@ Auto-annotation is a powerful feature of SAM 2, enabling users to generate segme ### How to Auto-Annotate with SAM 2 +

+
+ +
+ Watch: Auto Annotation with Meta's Segment Anything 2 Model using Ultralytics | Data Labeling +

+ To auto-annotate your dataset using SAM 2, follow this example: !!! example "Auto-Annotation Example" diff --git a/docs/en/modes/benchmark.md b/docs/en/modes/benchmark.md index 587462df..149ad5a2 100644 --- a/docs/en/modes/benchmark.md +++ b/docs/en/modes/benchmark.md @@ -47,13 +47,13 @@ Once your model is trained and validated, the next logical step is to evaluate i


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- Watch: Ultralytics Modes Tutorial: Benchmark + Watch: Benchmark Ultralytics YOLO11 Models | How to Compare Model Performance on Different Hardware?

## Why Is Benchmarking Crucial? diff --git a/docs/en/tasks/pose.md b/docs/en/tasks/pose.md index 0523239f..7efe7414 100644 --- a/docs/en/tasks/pose.md +++ b/docs/en/tasks/pose.md @@ -13,28 +13,16 @@ Pose estimation is a task that involves identifying the location of specific poi The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually along with the confidence scores for each point. Pose estimation is a good choice when you need to identify specific parts of an object in a scene, and their location in relation to each other. - - - - - -
- -
- Watch: Pose Estimation with Ultralytics YOLO. - 		- -
- Watch: Pose Estimation with Ultralytics HUB. -
+

+
+ +
+ Watch: Ultralytics YOLO11 Pose Estimation Tutorial | Real-Time Object Tracking and Human Pose Detection +

!!! tip