Add executable files

.gitattributes

@@ -34,4 +34,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 *.axmodel filter=lfs diff=lfs merge=lfs -text
-*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+install/** filter=lfs diff=lfs merge=lfs -text

README.md

@@ -44,6 +44,8 @@ root@ax650 ~/root/yolo-master-esmoe # tree -L 3
 .
 |-- README.md
 |-- requirement.txt
+|-- install
+|   `-- ax_yolo_master_esmoe
 |-- models
 |   `-- ax650
 |       |-- config.json
@@ -76,7 +78,7 @@ root@ax650 ~/root/yolo-master-esmoe # tree -L 3
 pip install -r requirement.txt
 ```
 
-#### Inference with AX650 Host
+#### Inference with the web ui
 
 ```
 root@ax650 ~/root/yolo-master-esmoe # python web/app.py
@@ -98,6 +100,59 @@ Result：
 
 The demo for YOLO-Master-EsMoE-S and YOLO-Master-EsMoE-N is the same as YOLO-Master-EsMoE-M, just select the corresponding model in the WebApp.
 
+## Inference with the executable
+
+An executable is provided under `install/` for inference directly on the target board without Python. Run it on AX650:
+
+```
+ax_yolo_master_esmoe -m <model.axmodel> -i <image.jpg> 
+```
+
+Example:
+
+```
+ax_yolo_master_esmoe -m models/ax650/YOLO-Master-EsMoE-M/YOLO-Master-EsMoE-M.axmodel -i host.jpg --repeat 10
+--------------------------------------
+model file : models/ax650/YOLO-Master-EsMoE-M/YOLO-Master-EsMoE-M.axmodel
+image file : host.jpg
+img_h, img_w : 640 640
+--------------------------------------
+Engine creating handle is done.
+Engine creating context is done.
+Engine get io info is done.
+
+input size: 1
+    name:   images [UINT8] [RGB]
+        1 x 640 x 640 x 3
+
+
+output size: 4
+    name: /model.25/Concat_output_0 [FLOAT32]
+        1 x 64 x 8400
+
+    name: /model.25/Reshape_3_output_0 [FLOAT32]
+        1 x 80 x 6400
+
+    name: /model.25/Reshape_4_output_0 [FLOAT32]
+        1 x 80 x 1600
+
+    name: /model.25/Reshape_5_output_0 [FLOAT32]
+        1 x 80 x 400
+
+Engine alloc io is done.
+Engine push input is done.
+--------------------------------------
+post process cost time:12.02 ms
+--------------------------------------
+Repeat 10 times, avg time 23.42 ms, max_time 23.47 ms, min_time 23.38 ms
+--------------------------------------
+detection num: 4
+ 2:  92%, [   0,  151,  188,  410], car
+ 2:  89%, [  17,  162,  487,  475], car
+16:  62%, [ 197,  118,  364,  339], dog
+ 2:  62%, [ 592,  174,  639,  302], car
+--------------------------------------
+```
 
 ## Inference scripts
 
@@ -114,3 +169,6 @@ Run the axmodel inference on the target board:
 ```
 python infer/infer_axmodel.py --axmodel models/ax650/YOLO-Master-EsMoE-M/YOLO-Master-EsMoE-M.axmodel --post-onnx models/ax650/YOLO-Master-EsMoE-M/1_host_post_YOLO-Master-EsMoE-M.onnx --image football.jpg
 ```
+
+
+

install/axcl_aarch64/axcl_yolo_master_esmoe

@@ -0,0 +1,3 @@
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+oid sha256:c16527fc3a2057619bb78d119f110e349d741685c05637a607390599ba33fa42
+size 135168

install/host_650/ax_yolo_master_esmoe

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f93d15d128db951d5042174aa007723669ab357dcc9981aa777fccd425b3e4e
+size 117480