lsf pc update

Author: lsf

README.md

@@ -12,6 +12,10 @@
 
 ## Update News
 
+🚀（2024.06.06）支持目标检测算法Yolov10！
+
+🚀（2024.05.23）支持语义分割算法：PaddleSeg中的PP-LiteSeg、MobileSeg，轻量高效，适合部署！
+
 🚀（2023.12.03）支持全景驾驶感知算法 YOLOPv2，Better、Faster、Stronger ！
 
 🚀（2023.11.06）支持全景驾驶感知算法 YOLOP ！
@@ -28,7 +32,7 @@
 
 ## Highlights
 
-- 支持全景驾驶感知 YOLOPv2，目标检测 RT-DETR，Yolo 5/X/7/8 ，多目标跟踪 Bytetrack，单目标跟踪 OSTrack、LightTrack；
+- 支持全景驾驶感知 YOLOPv2，目标检测 RT-DETR，Yolov5/X/7/8/10 ，多目标跟踪 Bytetrack，单目标跟踪 OSTrack、LightTrack；
 - 预处理和后处理实现CUDA核函数，在 jetson 边缘端也能高性能推理；
 - 封装Tensor、Infer，实现内存复用、CPU/GPU 内存自动拷贝、引擎上下文管理、输入输出绑定等；
 - 推理过程实现生产者消费者模型，实现预处理和推理的并行化，进一步提升性能；
@@ -75,7 +79,7 @@
 
 2. compile engine
 
-   1. 下载 onnx 模型 [google driver](https://drive.google.com/drive/folders/16ZqDaxlWm1aDXQsjsxLS7yFL0YqzHbxT?usp=sharing) 
+   1. 下载 onnx 模型 [google driver](https://drive.google.com/drive/folders/16ZqDaxlWm1aDXQsjsxLS7yFL0YqzHbxT?usp=sharing) 或者按照教程导出，教程在各文件夹READEME
 
    2. ```bash
       cd Linfer/workspace
@@ -113,6 +117,9 @@
 |  yolov8_n  |   fp16    |  640x640   |  121.94   |
 |  yolov8_s  |   fp16    |  640x640   |   81.40   |
 |  yolov8_l  |   fp16    |  640x640   |    13     |
+| yolov10_n  |   fp16    |  640x640   |           |
+| yolov10_s  |   fp16    |  640x640   |           |
+| yolov10_l  |   fp16    |  640x640   |           |
 | rtdetr_r50 |   fp16    |  640x640   |    12     |
 | lighttrack |   fp16    |  256x256   |   90.91   |
 |  ostrack   |   fp16    |  256x256   |   37.04   |

apps/yolo/README.md

@@ -163,7 +163,7 @@
 1. 下载源码
 
    ```bash
-   https://github.com/ultralytics/ultralytics.git
+   git clone https://github.com/ultralytics/ultralytics.git
    cd ultralytics
    python setup.py develop
    ```
@@ -180,16 +180,16 @@
 3. 修改 ultralytics/engine/exporter.py 如下
 
    ```python
-   # line 313
-   # dynamic = self.args.dynamic
+   # line 312
+   output_names = ["output0", "output1"] if isinstance(self.model, SegmentationModel) else ["output"]
    dynamic = True
            if dynamic:
                dynamic = {'images': {0: 'batch'}}  # shape(1,3,640,640)
                if isinstance(self.model, SegmentationModel):
                    dynamic['output0'] = {0: 'batch', 2: 'anchors'}  # shape(1, 116, 8400)
                    dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'}  # shape(1,32,160,160)
                elif isinstance(self.model, DetectionModel):
-                   dynamic['output'] = {0: 'batch', 2: 'anchors'}  # shape(1, 84, 8400)
+                   dynamic['output'] = {0: 'batch'}  # shape(1, 84, 8400)
    ```
 
 4. 导出onnx

apps/yolov10/README.md

@@ -0,0 +1,33 @@
+## YoloV10 export onnx
+
+1. 下载源码
+
+   ```bash
+   git clone https://github.com/THU-MIG/yolov10
+   cd yolov10
+   pip install -e .
+   ```
+
+2. 修改 ultralytics/engine/exporter.py 如下
+
+   ```python
+   # line 369
+   output_names = ["output0", "output1"] if isinstance(self.model, SegmentationModel) else ["output"]
+   dynamic = True
+           if dynamic:
+               dynamic = {'images': {0: 'batch'}} 
+               if isinstance(self.model, SegmentationModel):
+                   dynamic['output0'] = {0: 'batch', 2: 'anchors'} 
+                   dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} 
+               elif isinstance(self.model, DetectionModel):
+                   dynamic['output'] = {0: 'batch'} 
+   ```
+
+3. 导出onnx
+
+   ```bash
+   yolo export model=yolov10s.pt format=onnx
+   ```
+
+
+