Update model card for yolov8-det

README.md

@@ -46,9 +46,11 @@ model-index:
 YOLOv8 Detection models optimized for edge AI deployment across multiple hardware platforms. All sizes from Nano to XLarge, in ONNX FP32 and TFLite INT8 formats, with platform-specific compiled models for NPU acceleration.
 
 Trained on [COCO 2017](https://test.edgefirst.studio/public/projects/1123/datasets/gallery/main?dataset=4819) (80 classes). Part of the [EdgeFirst Model Zoo](https://huggingface.co/EdgeFirst).
+> [!TIP]
 > **Training session**: [View on EdgeFirst Studio](https://test.edgefirst.studio/public/projects/1123/experiment/training/details?train_session_id=9488) — dataset, training config, metrics, and exported artifacts.
 
-> **Note**: Best-validated baseline.
+> [!NOTE]
+> Best-validated baseline.
 
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@@ -74,7 +76,7 @@ Full pipeline timing: pre-processing + inference + post-processing.
 |------|----------|---------------|----------------|-----------------|------------|-----|
 | — | — | — | — | — | — | — |
 
-> Measured with [EdgeFirst Perception](https://github.com/EdgeFirstAI) stack. Timing includes full GStreamer pipeline overhead.
+*Measured with [EdgeFirst Perception](https://github.com/EdgeFirstAI) stack. Timing includes full GStreamer pipeline overhead.*
 
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@@ -160,7 +162,7 @@ gst-launch-1.0 \
 ```
 
 
-> Full pipeline documentation: [EdgeFirst GStreamer Plugins](https://github.com/EdgeFirstAI/gstreamer)
+*Full pipeline documentation: [EdgeFirst GStreamer Plugins](https://github.com/EdgeFirstAI/gstreamer)*
 
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@@ -181,7 +183,7 @@ for det in results.detections:
     print(f"{det.label}: {det.confidence:.2f} at {det.bbox}")
 ```
 
-> [EdgeFirst HAL](https://github.com/EdgeFirstAI/hal) — Hardware abstraction layer with accelerated inference delegates.
+*[EdgeFirst HAL](https://github.com/EdgeFirstAI/hal) — Hardware abstraction layer with accelerated inference delegates.*
 
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@@ -197,19 +199,19 @@ CameraAdaptor variants are included alongside baseline RGB models:
 | `yolov8n-det-coco-grey.onnx` | GREY (1ch) | Monochrome / IR sensors |
 | `yolov8n-det-coco-yuyv.onnx` | YUYV (2ch) | Raw sensor bypass |
 
-> Train CameraAdaptor models with [EdgeFirst Studio](https://edgefirst.studio) — the CameraAdaptor layer is automatically inserted during training.
+*Train CameraAdaptor models with [EdgeFirst Studio](https://edgefirst.studio) — the CameraAdaptor layer is automatically inserted during training.*
 
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 ## Train Your Own with EdgeFirst Studio
 
-> Train on your own dataset with [**EdgeFirst Studio**](https://edgefirst.studio).
->
-> - **Free tier** includes YOLO training with automatic INT8 quantization and edge deployment
-> - Upload datasets via [EdgeFirst Recorder](https://github.com/EdgeFirstAI/recorder) or COCO/YOLO format
-> - AI-assisted annotation with auto-labeling
-> - CameraAdaptor integration for native sensor format training
-> - One-click deployment to edge devices via [EdgeFirst Client](https://github.com/EdgeFirstAI/client)
+Train on your own dataset with [**EdgeFirst Studio**](https://edgefirst.studio):
+
+- **Free tier** includes YOLO training with automatic INT8 quantization and edge deployment
+- Upload datasets via [EdgeFirst Recorder](https://github.com/EdgeFirstAI/recorder) or COCO/YOLO format
+- AI-assisted annotation with auto-labeling
+- CameraAdaptor integration for native sensor format training
+- Deploy trained models to edge devices via [EdgeFirst Client](https://github.com/EdgeFirstAI/client)
 
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