YOLO Vision 2024 updates https://ultralytics.com/events/yolovision (#15602)

Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
3 months ago · 0635a40b98
parent be9fe124e3
commit 0635a40b98
4 changed files with 4 additions and 6 deletions
--- a/README.md
+++ b/README.md
@ -1,6 +1,6 @@
 <div align="center">
  <p>
-    <a href="https://github.com/ultralytics/assets/releases/tag/v8.2.0" target="_blank">
+    <a href="https://www.ultralytics.com/events/yolovision" target="_blank">
      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="YOLO Vision banner"></a>
  </p>

--- a/README.zh-CN.md
+++ b/README.zh-CN.md
@ -1,6 +1,6 @@
 <div align="center">
  <p>
-    <a href="https://github.com/ultralytics/assets/releases/tag/v8.2.0" target="_blank">
+    <a href="https://www.ultralytics.com/events/yolovision" target="_blank">
      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="YOLO Vision banner"></a>
  </p>

--- a/docs/en/index.md
+++ b/docs/en/index.md
@ -5,7 +5,7 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, deep learni
 ---

 <div align="center">
-<a href="https://github.com/ultralytics/assets/releases/tag/v8.2.0" target="_blank"><img width="1024%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
+<a href="https://www.ultralytics.com/events/yolovision" target="_blank"><img width="1024%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
 <a href="https://docs.ultralytics.com/zh/">中文</a> |
 <a href="https://docs.ultralytics.com/ko/">한국어</a> |
 <a href="https://docs.ultralytics.com/ja/">日本語</a> |
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@ -84,9 +84,7 @@ def test_fastsam(task="segment", model=WEIGHTS_DIR / "FastSAM-s.pt", data="coco8
        new_masks, _ = Predictor.remove_small_regions(everything_results[0].masks.data, min_area=20)

        # Run inference with bboxes and points and texts prompt at the same time
-        results = sam_model(
-            source, bboxes=[439, 437, 524, 709], points=[[200, 200]], labels=[1], texts="a photo of a dog"
-        )
+        sam_model(source, bboxes=[439, 437, 524, 709], points=[[200, 200]], labels=[1], texts="a photo of a dog")


 def test_mobilesam():