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 osimport ultralytics import onnx from os import pathmodel = ultralytics.YOLO("model.pt")model = ultralytics.YOLO("yolov8n.pt")model.export(format="onnx") # Creates model.onnxmodel.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" }, {