Download YOLOv8 official code ultralytics/ultralytics

$ git clone https://github.com/ultralytics/ultralytics.git

Refer README.md to train a YOLOv8n model.

The SDK only supports python3.6 or python3.8, here is an example of creating a virtual environment for python3.8.

Install python packages.

$ sudo apt update
$ sudo apt install python3-dev python3-numpy

Follow this docs to install conda.

Then create a virtual environment.

$ conda create -n npu-env python=3.8
$ conda activate npu-env     #activate
$ conda deactivate           #deactivate

Download Tool from rockchip-linux/rknn-toolkit2.

$ git clone https://github.com/rockchip-linux/rknn-toolkit2.git
$ git checkout 9ad79343fae625f4910242e370035fcbc40cc31a

Install dependences and RKNN toolkit2 packages,

$ cd rknn-toolkit2
$ sudo apt-get install python3 python3-dev python3-pip
$ sudo apt-get install libxslt1-dev zlib1g-dev libglib2.0 libsm6 libgl1-mesa-glx libprotobuf-dev gcc cmake
$ pip3 install -r doc/requirements_cp38-*.txt
$ pip3 install packages/rknn_toolkit2-*-cp38-cp38-linux_x86_64.whl

After training model, modify ultralytics/ultralytics/nn/modules/head.py as follows.

head.py

diff --git a/ultralytics/nn/modules/head.py b/ultralytics/nn/modules/head.py
index 0b02eb3..0a6e43a 100644
--- a/ultralytics/nn/modules/head.py
+++ b/ultralytics/nn/modules/head.py
@@ -42,6 +42,9 @@ class Detect(nn.Module):
 
     def forward(self, x):
         """Concatenates and returns predicted bounding boxes and class probabilities."""
+        if torch.onnx.is_in_onnx_export():
+            return self.forward_export(x)
+
         shape = x[0].shape  # BCHW
         for i in range(self.nl):
             x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
@@ -80,6 +83,15 @@ class Detect(nn.Module):
             a[-1].bias.data[:] = 1.0  # box
             b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)
 
+    def forward_export(self, x):
+        results = []
+        for i in range(self.nl):
+            dfl = self.cv2[i](x[i]).contiguous()
+            cls = self.cv3[i](x[i]).contiguous()
+            results.append(torch.cat([cls, dfl], 1).permute(0, 2, 3, 1).unsqueeze(1))
+            # results.append(torch.cat([cls, dfl], 1))
+        return tuple(results)
+

Create a python file written as follows to export onnx model.

export.py

from ultralytics import YOLO
model = YOLO("./runs/detect/train/weights/best.pt")
results = model.export(format="onnx")

Enter rknn-toolkit2/examples/onnx/yolov5 and modify test.py as follows.

test.py

# Create RKNN object
rknn = RKNN(verbose=True)
 
# pre-process config
print('--> Config model')
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
print('done')
 
# Load ONNX model
print('--> Loading model')
ret = rknn.load_onnx(model='./yolov8n.onnx')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
    print('Build model failed!')
    exit(ret)
print('done')
 
# Export RKNN model
print('--> Export rknn model')
ret = rknn.export_rknn('./yolov8n.rknn')
if ret != 0:
    print('Export rknn model failed!')
    exit(ret)
print('done')

Run test.py to generate rknn model.

$ python3 test.py

Clone the source code from our khadas/edge2-npu.

$ git clone https://github.com/khadas/edge2-npu

$ sudo apt update
$ sudo apt install cmake libopencv-dev

Put yolov8n.rknn in edge2-npu/C++/yolov8n/data/model.

# Compile
$ bash build.sh
 
# Run
$ cd install/yolov8n
$ ./yolov8n data/model/yolov8n.rknn data/img/bus.jpg

Put yolov8n.rknn in edge2-npu/C++/yolov8n_cap/data/model.

# Compile
$ bash build.sh
 
# Run
$ cd install/yolov8n_cap
$ ./yolov8n_cap data/model/yolov8n_cap.rknn 33