Merge branch 'main' into uv-benchmarks

.github/workflows/ci.yaml

@@ -212,7 +212,8 @@ jobs:
       - uses: actions/checkout@v4
       - uses: astral-sh/setup-uv@v4
       - name: Install requirements
-        run: uv pip install --system . pytest-cov
+        shell: bash # for Windows compatibility
+        run: uv pip install --system -e . pytest-cov
       - name: Check environment
         run: |
           yolo checks

docs/en/integrations/sony-imx500.md

@@ -162,7 +162,7 @@ cd examples/imx500
 Step 3: Run YOLOv8 object detection, using the labels.txt file that has been generated during the IMX500 export.
 
 ```bash
-python imx500_object_detection_demo.py --model <path to network.rpk> --fps 25 --bbox-normalization --ignore-dash-labels --bbox-order xy –labels <path to labels.txt>
+python imx500_object_detection_demo.py --model <path to network.rpk> --fps 25 --bbox-normalization --ignore-dash-labels --bbox-order xy --labels <path to labels.txt>
 ```
 
 Then you will be able to see live inference output as follows