Update README.md

README.md

@@ -1,3 +1,6 @@
+---
+pipeline_tag: object-detection
+---
 # YOLOv12‑x Object Detector
 
 Ultralytics’s attention‑centric, real‑time object detection model **YOLOv12‑x** is now available on Hugging Face.
@@ -6,53 +9,98 @@ Ultralytics’s attention‑centric, real‑time object detection model **YOLOv1
 
 ## 🧠 Model Description
 
-YOLOv12‑x builds on the YOLO12 family by combining **Area Attention** and **R‑ELAN** modules to deliver state‑of‑the‑art detection accuracy with fewer parameters and FLOPs. Optional **FlashAttention** integration further reduces memory access overhead and boosts inference speed on modern NVIDIA GPUs :contentReference[oaicite:0]{index=0}.
+YOLOv12‑x builds on the YOLO12 family by combining **Area Attention** and **R‑ELAN** modules to deliver state‑of‑the‑art detection accuracy with fewer parameters and FLOPs. Optional **FlashAttention** integration further reduces memory access overhead and boosts inference speed on modern NVIDIA GPUs citeturn0view0.
 
 ---
 
 ## ⚙️ Requirements
 
-- **Python** ≥ 3.8  
-- **PyTorch** ≥ 1.10 (CUDA‑enabled)  
-- **CUDA** ≥ 11.2 compatible GPU  
-- **Optional**: FlashAttention (install via `pip install flash-attn`)  
-- **Recommended GPU architectures** for FlashAttention support:  
-  - Turing (e.g. T4, Quadro RTX)  
-  - Ampere (RTX 30 series, A30/40/100)  
-  - Ada Lovelace (RTX 40 series)  
-  - Hopper (H100/H200) :contentReference[oaicite:1]{index=1}  
-- **System specs**: ≥ 8 GB RAM, ≥ 50 GB free disk
+* **Python** ≥ 3.8
+* **PyTorch** ≥ 1.10 (CUDA‑enabled)
+* **CUDA** ≥ 11.2 compatible GPU
+* **Optional**: FlashAttention (install via `pip install flash-attn`)
+* **Recommended GPU architectures** for FlashAttention support:
+
+  * Turing (e.g. T4, Quadro RTX)
+  * Ampere (RTX 30 series, A30/40/100)
+  * Ada Lovelace (RTX 40 series)
+  * Hopper (H100/H200) citeturn0view0
+* **System specs**: ≥ 8 GB RAM, ≥ 50 GB free disk
 
 ---
 
-## 🚀 Usage
+## 🚀 Installation & Usage
+
+```bash
+pip install ultralytics
+# (Optional for FlashAttention)
+pip install flash-attn
+```
+
+**Python example**:
+
+```python
+from ultralytics import YOLO
+
+# Load a COCO-pretrained YOLO12x model
+model = YOLO("yolo12x.pt")
+
+# Train the model on the COCO8 example dataset for 100 epochs
+results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
 
+# Run inference with the YOLO12n model on the 'bus.jpg' image
+results = model("path/to/bus.jpg")
+```
+
+**CLI example**:
+
+```bash
+yolo detect predict model=yolov12x.pt source=test.jpg imgsz=640 conf=0.25
+```
+
+---
 
 ## 📊 Performance & Use Cases
+
 Benchmarked on COCO val2017 at 640 × 640 resolution on an NVIDIA T4 GPU:
 
-Model	mAP@0.5:0.95	Latency (ms)	Params (M)	FLOPs (B)
-YOLO12‑x	55.2 %	11.79	59.1	199.0
+| Model    | mAP\@0.5:0.95 | Latency (ms) | Params (M) | FLOPs (B) |                   |
+| -------- | ------------- | ------------ | ---------- | --------- | ----------------- |
+| YOLO12‑x | 55.2 %        | 11.79        | 59.1       | 199.0     | citeturn0view0 |
 
 YOLOv12‑x excels in scenarios demanding both high accuracy and near‑real‑time throughput:
 
-Autonomous vehicles
+* Autonomous vehicles
+* Industrial inspection
+* Surveillance & security systems
 
-Industrial inspection
-
-Surveillance & security systems
+---
 
+## 📚 References
 
+```bibtex
 @article{tian2025yolov12,
   title={YOLOv12: Attention-Centric Real-Time Object Detectors},
   author={Tian, Yunjie and Ye, Qixiang and Doermann, David},
   journal={arXiv preprint arXiv:2502.12524},
   year={2025}
 }
+```
 
-```python
-from ultralytics import YOLO
+---
 
-model = YOLO("yolov12x.pt")
-results = model.predict(source="test.jpg", imgsz=640, conf=0.25)
-results[0].show()
+## 📝 Summary
+
+| Feature            | Details                                       |
+| ------------------ | --------------------------------------------- |
+| **Model**          | YOLOv12‑x                                     |
+| **Architecture**   | Area Attention + R‑ELAN                       |
+| **FlashAttention** | Optional (GPU‑accelerated)                    |
+| **Requirements**   | Python ≥ 3.8, PyTorch ≥ 1.10, CUDA ≥ 11.2     |
+| **Use Cases**      | Real‑time object detection with high accuracy |
+
+```plaintext
+Files:
+├── yolov12x.pt          # Trained model weights
+├── README.md            # This file
+```