Update README.md

README.md

@@ -41,9 +41,30 @@ For model conversion and deployment guidance:
 
 ## How to Use
 
-BEVFormer requires multi-view camera inputs (typically 6 views: front, front-left, front-right, back, back-left, back-right).
-```
-```
+BEVFormer requires multi-view camera inputs (typically 6 views: front, front-left, front-right, back, back-left, back-right). 
+
+### Prerequisites
+
+1.  **Environment:** Ensure you have the required Python environment activated (e.g., using Conda or a virtual environment) with the following core packages installed:
+    * **NPU Runtime:** `axengine` (PyAXEngine)
+    * **Core Libraries:** `numpy` (>= 1.22.0), `opencv-python` (`cv2`), `tqdm`, and `cffi`.
+    * 
+    *(Recommended: Use a dedicated Conda environment to manage these dependencies.)*
+
+2.  **Model/Data:** Ensure the compiled `.axmodel`, `inference_config.json`, and input data (`inference_data/`) are available on the host.
+
+### Inference Command
+
+Run the inference script by providing the compiled model, configuration, and data directory.
+
+```bash
+# 1. 激活 Conda 环境 (如果您之前创建了隔离环境)
+# conda activate ax_env 
+
+# 2. 执行推理
+python inference_axmodel.py compiled.axmodel inference_config.json inference_data/ --output-dir inference_results
+
+
 ### Inference with AX650 Host
 ```
 (base) root@ax650:~/data# python inference_axmodel.py compiled.axmodel inference_config.json inference_data/ --output-dir ./inference_results
@@ -67,3 +88,7 @@ Save scene results: 100%|██████████████████
 ### Results
 The model generates a 3D detection map projected onto the Bird's-Eye-View plane. Results are saved as images and videos which visualize the ego-vehicle and surrounding detected objects.
 
+**Example Visualization:**
+![BEVFormer Detection Result GIF](output.gif)
+
+