Update README.md

README.md

@@ -1,10 +1,10 @@
 ---
 base_model:
 - ultralytics/yolo11
-model_name: yolo11-bloodcell-onnx
+model_name: yolo11-blood_cell-onnx
 tags:
-- sft
 - object-detection
+- sft
 - yolov11
 - medical-imaging
 - blood-cell
@@ -43,7 +43,7 @@ The first **10 layers were frozen** to retain pretrained spatial features, and t
 
 ## 🩺 Example Detection
 
-![Blood Cell Detection](https://huggingface.co/Jesteban247/yolo11-bloodcell-onnx/resolve/main/Blood.png)
+![Blood Cell Detection](https://huggingface.co/Jesteban247/yolo11-blood_cell-onnx/resolve/main/Blood.png)
 
 ---
 
@@ -60,11 +60,4 @@ The first **10 layers were frozen** to retain pretrained spatial features, and t
 | **Model Size (MB)** | 5.2 |
 
 > FP16 inference maintained **identical accuracy** to FP32 while reducing latency.  
-> Layer freezing improved training stability and avoided overfitting on the limited dataset.
-
----
-
-## 💡 Observations
-
-- **Layer freezing (10 layers)** preserved base detection structure and sped up convergence.  
-- **FP16 precision** offers optimal trade-off between speed and performance.  
+> Layer freezing improved training stability and avoided overfitting on the limited dataset.