roundabout,
created on Monday, 28 April 2025, 19:48:21 (1745869701),
received on Monday, 28 April 2025, 19:48:27 (1745869707)
Author identity: vlad <vlad.muntoiu@gmail.com>
b7ab20ec78a7dbc0fb8c2181f7253227a0f48783
gitignore
@@ -1,2 +1,3 @@
data/
val_data/
hailo/
hailo_conversion.py
@@ -1,5 +1,33 @@
import subprocess
import os
import ultralytics
import onnx
from os import path
model = ultralytics.YOLO("model.pt")
model = ultralytics.YOLO("yolov8n.pt")
model.export(format="onnx") # Creates model.onnx
model.export(format="onnx", imgsz=640, opset=11)
model = onnx.load("/home/vlad/recycle4me/yolov8n.onnx")
print("Inputs:")
for i in model.graph.input:
print(i.name, [d.dim_value for d in i.type.tensor_type.shape.dim])
print("Outputs:")
for o in model.graph.output:
print(o.name, [d.dim_value for d in o.type.tensor_type.shape.dim])
current_dir = os.getcwd()
onnx_path = os.path.join(current_dir, "yolov8n.onnx")
val_data_path = os.path.join(current_dir, "val_data")
result = subprocess.run([
path.expanduser(".venv/bin/hailomz"),
"compile",
"yolov8n",
"--hw-arch", "hailo8l",
"--ckpt", onnx_path,
"--calib-path", val_data_path,
"--start-node-names", "images",
"--end-node-names", "output0",
"--classes", "7",
], check=True)
main.ipynb
@@ -4240,7 +4240,7 @@
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "torch.save(model.state_dict(), \"model.pt\")",
"source": "torch.save(model.state_dict(), \"yolov8n.pt\")",
"id": "138880b6a127671a"
},
{