Merge branch 'main' into cli-info

docs/en/datasets/detect/coco.md

@@ -157,6 +157,8 @@ Pretrained YOLO11 models on the COCO dataset can be downloaded from the links pr
 - [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt)
 - [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt)
 - [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt)
+- [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt)
+- [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt)
 
 These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.
 

docs/en/datasets/segment/coco.md

@@ -141,13 +141,7 @@ The COCO-Seg dataset includes several key features:
 
 The COCO-Seg dataset supports multiple pretrained YOLO11 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics:
 
-| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
-| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
-| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
-| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
-| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
-| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
-| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
+{% include "macros/yolo-seg-perf.md" %}
 
 ### How is the COCO-Seg dataset structured and what subsets does it contain?
 

docs/en/models/mobile-sam.md

@@ -4,7 +4,7 @@ description: Discover MobileSAM, a lightweight and fast image segmentation model
 keywords: MobileSAM, image segmentation, lightweight model, fast segmentation, mobile applications, SAM, ViT encoder, Tiny-ViT, Ultralytics
 ---
 
-![MobileSAM Logo](https://github.com/ChaoningZhang/MobileSAM/blob/master/assets/logo2.png)
+![MobileSAM Logo](https://raw.githubusercontent.com/ChaoningZhang/MobileSAM/master/assets/logo2.png)
 
 # Mobile Segment Anything (MobileSAM)
 

docs/en/models/sam-2.md

@@ -65,7 +65,7 @@ SAM 2 sets a new benchmark in the field, outperforming previous models on variou
 - **Memory Mechanism**: Includes a memory encoder, memory bank, and memory attention module. These components collectively store and utilize information from past frames, enabling the model to maintain consistent object tracking over time.
 - **Mask Decoder**: Generates the final segmentation masks based on the encoded image features and prompts. In video, it also uses memory context to ensure accurate tracking across frames.
 
-![SAM 2 Architecture Diagram](https://github.com/facebookresearch/segment-anything-2/blob/main/assets/model_diagram.png)
+![SAM 2 Architecture Diagram](https://raw.githubusercontent.com/facebookresearch/sam2/refs/heads/main/assets/model_diagram.png)
 
 ### Memory Mechanism and Occlusion Handling
 

pyproject.toml

@@ -62,7 +62,7 @@ classifiers = [
 
 # Required dependencies ------------------------------------------------------------------------------------------------
 dependencies = [
-    "numpy>=1.23.0,<2.0.0", # temporary patch for compat errors https://github.com/ultralytics/yolov5/actions/runs/9538130424/job/26286956354
+    "numpy>=1.23.0", # temporary patch for compat errors https://github.com/ultralytics/yolov5/actions/runs/9538130424/job/26286956354
     "matplotlib>=3.3.0",
     "opencv-python>=4.6.0",
     "pillow>=7.1.2",

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.3.3"
+__version__ = "8.3.4"
 
 import os